• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Automatic Aggregation Enhanced Affinity Propagation Clustering Based on Mutually Exclusive Exemplar Processing

    2023-12-12 15:51:00ZhihongOuyangLeiXueFengDingandYongshengDuan
    Computers Materials&Continua 2023年10期

    Zhihong Ouyang,Lei Xue,Feng Ding and Yongsheng Duan

    Electronic Countermeasure Institute,National University of Defense Technology,Hefei,230037,China

    ABSTRACT Affinity propagation (AP) is a widely used exemplar-based clustering approach with superior efficiency and clustering quality.Nevertheless,a common issue with AP clustering is the presence of excessive exemplars,which limits its ability to perform effective aggregation.This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters,without changing the similarity matrix or customizing preference parameters,as done in existing enhanced approaches.An automatic aggregation enhanced affinity propagation(AAEAP)clustering algorithm is proposed,which combines a dependable partitioning clustering approach with AP to achieve this purpose.The partitioning clustering approach generates an additional set of findings with an equivalent number of clusters whenever the clustering stabilizes and the exemplars emerge.Based on these findings,mutually exclusive exemplar detection was conducted on the current AP exemplars,and a pair of unsuitable exemplars for coexistence is recommended.The recommendation is then mapped as a novel constraint,designated mutual exclusion and aggregation.To address this limitation,a modified AP clustering model is derived and the clustering is restarted,which can result in exemplar number reduction,exemplar selection adjustment,and other data point redistribution.The clustering is ultimately completed and a smaller number of clusters are obtained by repeatedly performing automatic detection and clustering until no mutually exclusive exemplars are detected.Some standard classification data sets are adopted for experiments on AAEAP and other clustering algorithms for comparison,and many internal and external clustering evaluation indexes are used to measure the clustering performance.The findings demonstrate that the AAEAP clustering algorithm demonstrates a substantial automatic aggregation impact while maintaining good clustering quality.

    KEYWORDS Clustering;affinity propagation;automatic aggregation enhanced;mutually exclusive exemplars;constraint

    1 Introduction

    In the field of unsupervised learning,cluster analysis is a crucial technology.It aims at separating elements into distinct categories based on certain similarity assessment criteria and result evaluation indexes.Elements with high similarity are commonly classified into the same cluster,whereas elements in different clusters have lower similarity.With the fast development of information technology,clustering is required to address realistic challenges in numerous technical fields,including image segmentation,text mining,network analysis,target recognition,trajectory analysis,and gene analysis,which in turn enhance the continuous development of clustering approaches[1–4].

    The existing clustering approaches can be generally divided into subsequent categories.The partitioning-based approach usually assumes that the data set must be classified intoKclusters,and iteratively searches for the optimal centers and data partitioning through certain criteria.The benefits are comprehensible,but usually,there is a dearth of prior knowledge about the number of clusters.The representative approaches are K-means[5],K-medoids[6],and Fuzzy C-means(FCM)[7].Hierarchical clustering approaches mainly combine data or split clusters according to certain criteria and ultimately represent the findings in a tree structure.Balanced iterative reducing and clustering using hierarchies [8] and clustering using representatives [9] are the representatives.Their most visible advantage is lucid logic,but the drawbacks are that the clustering process is irreversible and requires vast calculations.Density-based clustering approaches mainly consider the density of each data point within a certain range.Data points with superior density are commonly identified as centers,whereas those with very inferior density are considered outliers.These approaches can better adapt to clusters with disparate shapes and do not need to specify the number of clusters in advance.However,their clustering findings are sensitive to parameters,including distance range and density threshold.The representatives are density-based spatial clustering of applications with noise [10],ordering points to identify the clustering structure[11],and clustering by quick search and finding of density peaks(DP)[12].The basic idea of grid-based clustering approaches is to divide the data space into a grid structure of numerous cells and achieve classification by processing cells[13].The benefits are fast calculation and good data adaptability.The drawbacks are that the data structure is ignored and the clustering findings are also influenced by the cell shape and size.Statistical information grid[14]and clustering in quest[15]are well-known grid-based clustering approaches.In addition to the four types of clustering approaches,some approaches based on specific mathematical models have also been widely used,including Gaussian Mixture Model-based clustering [16],spectral clustering(SC)based on graph theory[17],and affinity propagation(AP)clustering based on message passing mechanism[18].

    The AP clustering in which we are interested comes from the idea of belief propagation [19–21],and it is currently an extremely popular and potent exemplar-based clustering approach.To maximize total similarity,AP locates a set of exemplars and establishes corresponding relationships between exemplars and other data points.The message-passing mechanism is adopted to address the optimization challenge,which has been expressed in a more intuitive binary graphical model[22,23].First,AP considers all data points as potential exemplars.The messages designated responsibility and availability are then transmitted back and forth between the data under the objective function and limitations.Finally,the exemplars are chosen and each data point finds its most suitable exemplar.AP offers several advantages,including simple initialization,the absence of a requirement to specify the cluster number,superior clustering quality,and high computational efficiency.AP has been widely employed in manifold aspects such as face recognition [24,25],document clustering [26,27],neural network classifier [28],image analysis [29,30],grid system data clustering [31,32],small cell networks working analysis[33],manufacturing process analysis[34],bearing fault diagnosis[35–37],K-Nearest Neighbor (KNN) positioning [38],psychological research [39],radio environment map analysis[40],building evaluation[41],building materials analysis[42],interference management[43],genome sequences analysis[44],map generalization[45],signal recognizing[46],vehicle counting[47],indoor positioning [48],android malware analysis [49],marine water quality monitoring [50],and groundwater management[51].

    Classic AP also possesses some drawbacks including the sensitivity of preference selection.Generally,the median of all data similarity values is considered the preference value.Increasing this value leads to more exemplars,which represent more clusters.However,reducing the value leads to a decrease in the number of clusters.Furthermore,AP has superior performance on data sets with a regular distribution such as a spherical shape,but it is challenging to achieve good results in the tasks on data sets with a nonspherical distribution.Therefore,numerous studies have also concentrated on enhancing the AP clustering theory.TheK-AP algorithm [52] has customizability in terms of cluster number.It introduces a constraint ofKexemplars to make the clustering ultimately converge toKclusters.The rapid affinity propagation algorithm[53]enhances the clustering speed and quality,which separates the clustering process into coarsening phase,exemplar-clustering phase,and refining phase.The multi-exemplar affinity propagation algorithm [54] expands the single-exemplar model to a multi-exemplar one.It proposes the concept of super exemplar and addresses the multisubclass challenge.The stability-based affinity propagation algorithm[55]concentrates on addressing the preference selection challenge.It offers a new clustering stability measure and automatically sets preference values,which can generate stable clustering results.The soft-constraint semi-supervised affinity propagation algorithm [56] adds supervision based on AP clustering and implements soft constraints,which can generate more accurate results.Another rapid affinity propagation algorithm[57] enhances the efficiency by compressing the similarity matrix.The adjustable preference affinity propagation algorithm[58]mainly concentrates on preference selection and parameter sensitivity of AP.The message-passing model is derived under additional preference-adjusting constraints,and it results in automatic preference adjustment and better clustering performance.Through densityadaptive preference estimation,an adaptive density distribution-inspired AP clustering algorithm[59]addresses the challenge of preference selection.Additionally,to address the nonspherical cluster problem,the algorithm uses a similarity measurement strategy based on the nearest neighbor search to describe the data set structure.The adaptive spectral affinity propagation algorithm[60]discusses why AP is unsuitable for nonspherical clusters and proposes a model selection procedure that can adaptively determine the number of clusters.

    It is clear from the foregoing theoretical development of AP that the enhanced algorithms mainly concentrate on similarity matrix construction,preference selection,and application of nonspherical data clustering.However,there are relatively few studies on the aggregation ability of AP clustering.Aggregation is the important goal of clustering,and it is patently crucial.The enhancement of aggregation ability represents a reduction in cluster number,which is anticipated for numerous application scenarios.People prefer to obtain results such as sunrise and sunset rather than sunrise with fishing boats,sunrise with more clouds,sunset with faster waves,and peaceful sunset,as mentioned in the example of multi-subclass image clustering in[54].Multiple clusters are undoubtedly significant since they offer more detailed classification and richer cluster information.We just want to study that reducing the cluster number is equally valuable as it can offer more general and comprehensive information.

    However,numerous investigations have demonstrated that AP clustering cannot independently converge to a relatively small cluster size.As mentioned above,reducing preference values will lead to fewer clusters,although there is no explicit analytical correlation between the preference value and cluster number.By revising preferences and similarity matrices based on analyzing the data set structure or data point density,some enhanced algorithms can also reduce the number of clusters to some extent.However,we should recognize that the revision has certain subjectivity,as it indicates that we already hope some specific data points will become the final exemplars.Additionally,we cannot guarantee the accuracy of the structure analysis.The difficult tasks are how to set preference values for these exemplars and how to define the function to compute the similarity between exemplars and other data points,assuming that the analysis is accurate enough to exclude potential exemplars.

    We concentrate on making the AP cluster show stronger aggregation while ensuring clustering quality.It can be imagined that the aggregation involves merging clusters,but classic AP clustering does not know which clusters can be merged;therefore,it needs a reliable information source to tell it.We anticipate that the information that can enhance aggregation is automatically generated and objective,without human involvement.An automatic aggregation enhanced affinity propagation(AAEAP)clustering algorithm based on mutually exclusive exemplar processing is proposed based on the foregoing considerations.The general idea of the AAEAP is as follows.

    First,we select a dependable partitioning clustering approach such as FCM clustering,and allow it to generateMclusters when the AP clustering stabilizes and converges toMclusters.Fig.1 shows various possible differences between AP and FCM clustering findings when the number of clusters is the same.In Fig.1a,both AP and FCM generate two clusters,but the exemplars are disparate,leading to substantial differences in the classification of other data points.The exemplars of AP are just ordinary points in the two clusters of FCM.Fortunately,neither cluster of FCM contains both exemplars of AP.In Fig.1b,the classification findings of AP and FCM are very similar and only a few data points demonstrate differences in selecting the cluster.These situations are understandable and tolerable.However,in Fig.1c,there are substantial differences in the clustering findings between AP and FCM.Particularly,the blue cluster generated by FCM contains the blue and green exemplars of AP,and we consider it intolerable.Thus,these AP exemplars contained in the same FCM cluster are determined to be mutually exclusive.It is essential to modify the number of AP clusters,and these mutually exclusive exemplars should not exist as exemplars at the same time.

    Figure 1:Possible changes in the findings of various clustering approaches.(a)Changes in exemplars.(b)Changes in data assignment.(c)Two exemplars classified in one cluster

    By adding a novel constraint and altering the message iteration model,we addressed the foregoing challenge.Then,the adjusted clustering converges stably again based on the new model.Until there are no more conflicting situations,mutual exclusion exemplar detection and clustering of the entire clustering process must be repeated.Some standard classification data sets were adopted to examine and validate the proposed AAEAP algorithm,and six clustering assessment indexes were employed to compare the quality of the result between AAEAP and the other eight clustering algorithms.

    The key contributions of the proposed work are as follows:

    ? We propose a method that can improve the aggregation ability of AP clustering.The core is to employ a partitioning clustering algorithm to detect mutually exclusive exemplars and then reliably guide AP to combine clusters.

    ? The detection information output by the partitioning clustering approach is mapped as a mutual exclusion and aggregation clustering constraint,and the new message iteration model is derived in detail.

    ? The overall aggregation improved clustering process is automated and does not need manual intervention,nor does it involve potential exemplar selection and preference revision,which enhances the algorithm’s applicability.

    The remainder of this study is organized as follows.Section 2 provides a brief review of AP.Section 3 introduces the proposed AAEAP algorithm.Section 4 presents the experimental findings on standard classification data sets.Section 5 concludes the study work.

    2 Affinity Propagation

    AP clustering is an exemplar-based clustering algorithm that simultaneously considers all data points as potential exemplars and exchanges messages between them until a high-quality exemplar set and corresponding cluster emerge.AP was originally derived as an instance of the max-product algorithm in a loopy factor graph [19].A simplified max-sum (log-domain max-product) message update form was obtained by reducing then-ary messages to binary messages[18,22,23],making the message iteration process of the AP clustering clearer and easier to expand.

    Given a data setX={x1,x2,···,xN},a set of exemplars and their clusters are generated through AP clustering.The results are expressed using a binary matrixwherecijrepresents a binary variable,cij=1,if data pointiselects data pointjas an exemplar,otherwisecij=0.If data pointirepresents an exemplar,thencii=1,otherwisecii=0.

    The complete process of AP clustering is as follows: First,a data similarity measurement function is defined to compute the similaritys(i,j) between data pointsxiandxj,which can be a negative Euclidean distanceor user-defined,forming a similarity matrixS={s(i,j)}N×N i,j∈{1,···,N}.The diagonal elements of the similarity matrixSrepresent preference parameters,denoted aspk=s(k,k),k∈{1,···,N}.Based on theSfunction,Sijis defined to denote the similarity between data points and their exemplars.

    Meanwhile,we provide two basic constraints,IandE.We termIas the 1-of-N constraint,which indicates that each data point can only be assigned to one exemplar.Ican be naturally defined as follows:

    Erepresents the exemplar consistency constraint,which indicates that once a data point is selected as an exemplar by another point,it must select itself as an exemplar.Eis defined as

    As previously mentioned,the max-sum is the representation of thelog-domain max-product,and the 1-N constraint and the exemplar consistency constraint in the max-product model are changed as

    The goal of AP clustering is to maximizeFby finding a set of exemplars and corresponding data partitions.The process of finding high-quality exemplars is accomplished through the recursive transmission of messages.Fig.2 shows the transmission mechanisms for the two types of messages,r(i,j)anda(i,j).r(i,j)is referred to as responsibility,which is a message sent by data pointito candidate exemplarj,reflecting the accumulated evidence for how well-suited data pointjis to serve as the exemplar fori.a(i,j)is referred to as availability,which represents a message sent by candidate exemplarjto data pointi,reflecting the accumulated evidence for how suitable it would be for data pointito selectjas its exemplar.In other words,r(i,j) shows how strongly a data point favors one candidate exemplar over other candidates,anda(i,j)shows to what degree each candidate exemplar is available as a cluster center for one data point.

    Figure 2:Sending responsibility and availability messages

    The messages are initialized as 0 and updated as follows.

    It is necessary to increase the damping factorλ,with a value range of[0,1],to prevent oscillation during the message update process.By integrating the messagertandatof thetiteration with the messagert′,at′based onrtandat,the messagert+1andat+1of thet+1 iteration is obtained.

    Responsibility and availability update until convergence.AP ultimately outputs an assignment vector c=[c1,···,cN]andci=argmaxj[r(i,j)+a(i,j)].

    3 Automatic Aggregation Enhanced Affinity Propagation

    We propose an AAEAP clustering algorithm to overcome the difficulty of AP convergence to a small exemplar size.First,the entire framework of the algorithm is introduced.Then,the basic model is offered,including constraints,objective function,and factor graph.The message iteration is finally derived.

    3.1 Overall Framework

    The distinctive feature of AAEAP is that it uses a dependable partitioning clustering approach to detect whether there is a mutual exclusion situation in the exemplars produced by AP clustering.If there is,by adding a new constraint,the two mutually exclusive exemplars will not become exemplars and the number of clusters decreases.This will lead to the merging of clusters and present an aggregative state.The algorithm converges to a smaller exemplar size when there are no longer mutually exclusive exemplars.The basic framework of AAEAP is as follows.

    The input of the algorithm is an unclassified data setX={x1,···,xN},and each dataxiis a one-dimensional sequence havingdfeatures.Based on the negative Euclidean distance given by the classic AP,the similarity between two sequences is calculated,and ultimately the similarity matrixSis obtained.The computation approach of the similarity matrixScan be enhanced based on[53,59,60]to make AP more adaptable for data sets with nonspherical structures,although this is not the concentration of this research.The required parameters for AP clustering are initialized,including the maximum iteration numberNImax,stable convergence numberNIcvg,damping factorλ,and similarity matrixS.It is also required to clarify constraintsIandE,which determine the way messages are iterated.

    Then,the algorithm enters the message iteration.The algorithm will complete the first convergence based on constraintsIandE,and generate a set ofXEscontainingMexemplars due to the null initialization of the mutually exclusive exemplarsMEs.A dependable partitioning clustering approach including K-means,K-medoids,or FCM is used to detect mutually exclusive exemplars inXEs.Particularly,by employing the partitioning clustering approach to generateMclusters simultaneously,each cluster is checked to determine whether there are two or more AP exemplars.If so,the included exemplars are considered mutually exclusive.There may be situations where numerous pairs of mutually exclusive exemplars are detected.Only one pair is randomly selected for each iteration to simplify the computation.The foregoing process of partitioning clustering and detecting is processed through the functionIdentifyExclusion,which assigns a pair of mutually exclusive exemplars toMEs.

    We must define a new constraintDbased onMEs,which we call the mutual exclusion and aggregation constraint because of the existence of mutually exclusive exemplars.Its primary capability is to prevent mutually exclusive exemplars from becoming exemplars simultaneously again and reduce the overall number of exemplars by 1.A detailed description will be offered in the next section.The algorithm begins the second message iteration under the constraints ofI,E,andD.The expected aggregation impact will occur when stable convergence is achieved.Afterward,the previous steps will be repeated,including detecting the existence of mutually exclusive exemplars,updating constraint conditionsD,and restarting message iteration.The algorithm ends until there are no mutually exclusive exemplars left.The output of the AAEAP is the assignment vector c=[c1,···,cN],which is the same as the classic AP,andci=argmaxj[r(i,j)+a(i,j)].Finally,the clustering results should be examined to confirm the effectiveness of the algorithm.

    3.2 Basic Model

    Fig.3 shows the factor graph of the AAEAP algorithm.The similarity functionSand the three constraintsI,E,andDtogether influence the variable nodes.The similarity functionSonly influences each variable node in the graph separately,constraintIinfluences the rows in the graph,constraintEinfluences the columns,and constraintDaffects the diagonal.

    Figure 3:Factor graph of the AAEAP

    We present the model of the AAEAP algorithm in the max-product form.First,the constraints of AAEAP are given in detail.The 1-N constraintIand the exemplar consistency constraintEare the same as those of the classic AP.They are defined as

    The following concentrates on describing the newly added mutual exclusion and aggregation constraintD.ConstraintDhas two functions,one of which is to prevent mutually exclusive exemplars from becoming exemplars the next time,that is,they cannot coexist.The next clustering exemplar setXEswill either only have the exemplarp,only exemplarq,or neither exemplarspnorq,assuming thatpandqare mutually exclusive exemplars detected and recommended by partitioning clustering.The second function is to cause clustering to aggregation.Exemplarspandqserve as the core and representative of their clusters,reflecting the basic characteristics of the clusters.Crucially,it shows that there are substantial differences in the clusters generated aroundpandq.Therefore,mutual exclusion can be seen as a problem to the AP clustering findings,showing that some data points of the clusters represented bypandqcan be combined,while the remaining data points may select other exemplars,leading to a decrease in 1 in the exemplar number.

    According to the above considerations,constraintDis defined as

    Mdenotes the exemplar number generated by the previous iteration,pandqare two exemplars that satisfycpp=1 andcqq=1 in the above equation.However,cppandcqqcannot both be 1,and the exemplar number for the next iteration is constrained toM-1 when they are identified as mutually exclusive exemplars.ConstraintDis dynamically changing,as there are three variablesp,q,andM,which need to be defined before each iteration to determine the current messaging model.

    Then,the max-product objective function defined according to constraintsI,E,andDis

    3.3 Message Iteration

    Fig.4 illustrates the message iteration of AAEAP.As shown in Fig.4a,there are eight types of messages related to diagonal nodes.As illustrated in Fig.4b,there are six types of messages related to other nodes.

    Figure 4:Messages of the AAEAP.(a)Messages associated with cii.(b)Messages associated with cij

    Based on the max-product algorithm in the factor graph described in [23,52],the message representation from variable nodexito function nodef mis

    whereNe(xi)fmrepresents the set of functions related to the variablexiexcluding the functionf m,which can be considered as the neighborhood off m.

    The message from function nodef mto variable nodexiis expressed as

    whereNe(fm)represents the set of all variable nodes related to thef mfunction,whileNe(fm)xidoes not include variablexi.

    Based on Eq.(13),the messages sent by the variable nodes to the constraint functions in Fig.4 are

    Based on Eq.(14),the messagesθsent by the similarity functionSto the variable nodes are

    The messagesηsent to the variable nodes by theIconstraint function are

    The messagesαsent by theEconstraint function to the variable node are

    Additionally,theδmessages sent by theDconstraint function to the variable node are expressed as

    The above messages are binary messages that can be normalized by a scalar ratio[23,52]:βij(1)=βijandβij(0)=1.Then

    Similarly,ρij(0)=1ρij(1)=ρijandρij(0)=1,then

    Derivingδiimessages is relatively complex,althoughζij(1)=ζijandζij(0)=1,it is crucial to consider both the sub-constraints of mutual exclusion and the reduction of the exemplar number.Supposepandqare two mutually exclusive exemplars,without loss of generality,ifi=pand the exemplar number is reduced toM-1,then

    DefineR={1,···,p-1,p+1,···,q-1,q+1,···,N}.In Eq.(21),J1?RhasM-2 elements,K1?RhasN-Melements,and they satisfyJ1∩K1=?,J1∪K1=R,whileJ2?RhasM-1 elements,K2?RhasN-M-1 elements,and they satisfyJ2∩K2=?,J2∪K2=R.maxis the selection ofM-2 nodes with a value of 1 and the remainingN-Mnodes with a value of 0 fromN-2 variable nodes excludingcppandcqqin the factor graph.This selection scheme can maximize the continuous product ofζjj(1)messages andζkk(0)messages.Furthermore,it can be obtained that

    whereΦrepresents theζmessage set ofζjj,j∈Rmessages arranged in descending order,Φ1represents the maximum value inΦ,andΦM-1is theM-1 th largest value inΦ.

    And ifi/∈{p,q},then

    DefineR′={1,···,i-1,i+1,···,N}.In Eq.(23),J3?R′,{p,q}J3hasM-2 elements,K3?R′hasN-Melements and they satisfyJ3∩K3=?,J3∪K3=R′.Meanwhile,J4?R′,{p,q}J4hasM-1 elements,K4?R′hasN-M-1 elements and they satisfyJ4∩K4=?,J4∪K4=R′.So

    whereΓdenotes the set ofζmessages arranged in descending order ofζjj,j∈R′messages.Sincepandqare two mutually exclusive exemplars and the min{ζpp,ζqq}element must be eliminated fromΓ,ΓM-1is theM-1 th largest value inΓ.

    Through normalization,we have obtainedηii,ηij,αii,αij,δii.Becauseθij=es(i,j)it is easy to obtainβii=δii·θii·αii,βij=θij·αij,the expressions for other messages are

    Responsibility messages are expressed asr(i,j)=logρijand availability messages are expressed asa(i,j)=logαij[19–23,52].As a reference,mutual exclusion and aggregation messages are expressed asu(i)=logδii,v(i)=logζii.They are initialized as 0 and updated,respectively,as follows.

    whereV′represents the set ofv(j),j∈Rarranged in descending order,andV′(M-1)is theM-1 th largest value inV′.Similarly,V′′represents the set ofv(j),j∈R′arranged in descending order,andV′′(M-1)represents theM-1 th largest value inV′′.

    4 Results and Discussion

    The experiments were conducted on some standard classification data sets,and the clustering findings were evaluated using internal and external clustering efficiency evaluation indexes to verify the aggregation and accuracy of AAEAP.The clustering algorithms for comparison include classic AP,five enhanced AP algorithms,SC,and DP.

    4.1 Experimental Setting

    The data sets for our experiments are from the UC Irvine Machine Learning Repository [61],Knowledge Extraction based on Evolutionary Learning(KEEL)[62],and S-sets[63].Table 1 presents the brief information on these data sets.

    Table 1:Characteristics of the data sets

    We conduct min-max normalization on each column to ensure that the impact of each attribute on the clustering process and results are balanced before clustering.

    Besides classic AP,SC,and DP,there are also five enhanced AP algorithms for comparison,includingK-AP,AP clustering based on cosine similarity (CSAP) [48],adjusted preference AP clustering based on twice the median (TMPAP) [46],adjusted preference AP clustering based on quantile (QPAP) [50],and adaptive density distribution inspired affinity propagation clustering(ADDAP) [59].K-AP concentrates on customizing the number of clusters,CSAP concentrates on modifying the similarity matrixS,TMPAP and QPAP concentrate on modifying the preferences,while ADDAP measures the similarities based on nearest neighbor searching and modifies the preferences based on density.

    Both the clustering algorithms for comparison and the proposed AAEAP algorithm have been edited and implemented in Matlab R2016b.The pertinent settings are as follows:the similarity between data points is measured by Euclidean distance except for CSAP and ADDAP,the preferences of AAEAP,AP,and CSAP are established as the median of the total inter-point similarities,the damping factors of AAEAP,AP,and five enhanced AP algorithms are 0.9,theMparameter of QPAP is considered the value corresponding to the 5th quantile to reduce the number of exemplars,and the partitioning clustering approach necessary for AAEAP to detect mutually exclusive exemplars is FCM clustering.All experiments were conducted on Windows 7,with Intel?Core?i7-9700,memory size 16 GB.

    4.2 Clustering Evaluations

    An integral component of the clustering process is the validation of the clustering findings,and numerous indexes have been proposed to quantitatively evaluate the performance of clustering algorithms.Effectiveness evaluation indexes can be widely classified into two categories: internal and external evaluation indexes.The internal evaluation indexes primarily examine and assess the clustering results from aspects,including compactness,separation,and overlap,based on the structural information of the data set.The external evaluation indexes are mainly based on available prior information from the data set,including the cluster labels of all data points.The performance is evaluated by comparing the degree of correspondence between clustering results and external information.

    We use six extensive evaluation indexes,including Silhouette Coefficient (Sil) [64],In-Group Proportion(IGP)[65],Rand Index(RI)[66],Adjusted Rand Index(ARI)[66],F-measure(FM)[67],and Normalized Mutual Information (NMI) [54] to analyze the clustering results of the proposed AAEAP algorithm.Internal evaluation indexes are Sil and IGP,whereas external evaluation indexes are RI,ARI,FM,and NMI.

    Sil is an evaluation index based on compactness within a cluster and separation between clusters.For the data pointi,compute the average distance from it to other data points within the cluster,denoted asa(i).Compute the average distance from it to each other cluster,and use the minimum value denoted asb(i).Then,its silhouette coefficient can be expressed as

    The value range ofs(i)is[-1,1].Whens(i)approaches 1,it shows that the data point has a high degree of correspondence with the assigned cluster and is distant from other clusters.Furthermore,whens(i)approaches-1,it indicates that the data point is assigned to the wrong cluster.Traditionally,the Sil index of the overall clustering results is the average ofs(i)for all data points.

    IGP is defined as the proportion of each data point and its nearest neighboring point belonging to the same cluster.

    whereXis the data set,urepresents one cluster,jrepresents a data point inu,j1NNis the nearest neighbor point fromj,ClassX(j)=ClassX(j1NN)=udenotesjandj1NNbelongs to the same cluster,and#denotes the number of data points that meet the above conditions.ComputeIGP(u,X)for allncclusters,with a larger meanIGP_Mshowing superior clustering quality.

    RI is an external evaluation index that necessitates real classification informationC.AssumingKis the clustering results,adenotes the number of data pairs in the same cluster in bothCandK,andbdenotes the number of data pairs that are not in the same cluster whether inCorK,then RI is expressed as

    wherenrepresents the number of data points,denotes the number of data pairs that can be generated in the data set.The range of RI values is[0,1].A larger value indicates that the clustering results are more consistent with the real classification.

    ARI is an enhancement of RI,which examines clustering by computing the number of data pairs assigned to the same or different clusters in real labels and clustering findings.Compared to RI,ARI has higher discrimination.ARI is expressed as

    whereadenotes the number of data pairs that belong to the same cluster in both real labels and clustering findings,bdenotes the number of data pairs that belong to the same cluster in real labels but do not in clustering findings,cdenotes the number of data pairs that do not belong to the same cluster in real labels but belong to the same cluster in clustering findings,andddenotes the number of data pairs that are not in the same cluster,whether in real labels or clustering results.The range of ARI values is[-1,1],the larger the value,the better the clustering impact,and ARI equals 1,which signifies that clustering findings are completely consistent with real labels.

    FM integratesprecisionandrecallsto examine the clustering impact,which is expressed as

    whereprecision=,recall=.nkdenotes the number of data points in thekcluster of clustering findings,andnmdenotes the number of data points in themcluster of real classification anddenotes the number of data points shared by thekandmclusters.The larger the FM index value,the better the clustering impact.

    NMI assesses the similarity between the clustering results and the real labels from the perspective of information theory.AssumingUdenotes the clustering results containingkclusters,Vdenotes the real labels containingmclusters,andMI(U,V)is the mutual information between the clustering results and the real labels,then NMI is expressed as

    whereFis the geometric mean,nis the number of data points,ncis the data point number of theccluster in clustering results,nprepresents the data point number of thepcluster in real labels,andrepresents the number of the intersection of thecandpclusters.

    4.3 Experimental Results

    To demonstrate the automatic aggregation process of the AAEAP algorithm,we selected Iris and Wine data sets.Since the data dimensions of two data sets are greater than 3,we used the classic t-distributed stochastic neighbor embedding(t-SNE)approach[68]to achieve dimensionality reduction and then visualize the data to show the clustering process and aggregation impact.

    Fig.5 demonstrates the findings of the Iris data set.AAEAP converges based on the classic AP when the cluster numberNClassis 11.Then,AAEAP iterates eight times to automatically detect mutually exclusive exemplars and aggregates,stably converging to three clusters,consistent with the real category number of the Iris data set.

    Figure 5:Aggregation effect on Iris data set

    Fig.6 demonstrates the clustering and aggregation impact on the Wine data set.Based on classic AP,AAEAP converges to 21 clusters and then achieves automatic aggregation through mutually exclusive exemplar detection.Finally,with an equivalent number of real categories,AAEAP stably converges into three clusters.However,from the experiments,we discover that AAEAP cannot converge to three clusters every time,and there are also cases of six clusters.Similarly,there are cases of aggregation into three or four clusters for the Yeast data set.The main reason we examined is that AAEAP uses FCM partitioning clustering to detect mutually exclusive exemplars in the experiments,and the random initialization of FCM clustering causes instability in its classification,directly influencing the detection results of mutually exclusive exemplars;therefore,causing changes in AAEAP clustering results.A simple solution is to combine numerous FCM partitioning clustering results to weaken the randomness effect of FCM,and then conduct mutual exclusion detection and ensure the stability and comprehensiveness of the detection.Additionally,it shows the reliability of mutual exclusion detection,which we emphasized earlier.It is easy to imagine that dependable detection will bring accurate aggregation and better clustering quality.

    Figure 6:Aggregation effect on Wine data set

    Then,to measure the AAEAP clustering quality,we use clustering effectiveness evaluation indexes.Table 2 demonstrates the experimental findings on the Iris,Wine,Yeast,and S1 data sets.AAEAP can converge to the true cluster number,while AP,CSAP,QPAP,and TMPAP cannot.Compared to them,AAEAP has substantial benefits in terms of aggregation performance.ADDAP aims at obtaining the maximum Sil index value,but it cannot obtain the true numbers of clusters for these four data sets.The evaluation indexes of ADDAP are typically superior to those of AP,CSAP,QPAP,and TMPAP.However,AAEAP has better clustering quality than ADDAP,particularly external evaluation indexes,demonstrating that the AAEAP clustering results are closer to the real categories.The evaluation indexes ofK-AP,SC,and DP are mostly inferior to those of AAEAP for the Iris,Wine,and Yeast data sets,while their indexes for the S1 data set are perfect and slightly superior to those of AAEAP.

    Table 3 shows the findings on Segment and Glass data sets,where AAEAP does not converge to the real category numbers and exhibits excessive aggregation.Therefore,we also delineate the clustering evaluation findings whenK-AP,SC,and DP converge to the real category numbers.

    Table 3:The clustering results on segment and glass data sets

    Although the real category number of the Segment data set is 7,Table 3 shows that whenK-AP converges into 6 categories,the evaluation indexes are significantly superior to those of 7 categories because of fewer incorrect element classifications,and the entire clustering performance of AAEAP is better thanK-AP.However,some evaluation indexes of AP,CSAP,QPAP,and TMPAP are superior to those of AAEAP,but their aggregation performances are poor.The excessive aggregation of ADDAP is more serious,while the evaluation index values are generally good,particularly the internal indexes.The evaluation indexes are mostly inferior to those with seven categories when SC and DP converge into six categories.The performance of AAEAP is superior to that of SC and proximate to DP.The real number of categories is six for the Glass data set,and there is also a situation where the evaluation indexes whenK-AP converges into five categories are typically superior to the evaluation indexes with the real category number.Generally,the performance of AAEAP is better than that ofK-AP.ADDAP obtains fewer clusters,while its evaluation indexes are often inferior to those of AAEAP.Although AP,CSAP,QPAP,and TMPAP cannot converge into the real number of categories,their evaluation indexes are not bad.The evaluation indexes of SC and DP have their respective benefits,and the index values of AAEAP are often between the two.Fig.7 demonstrates the entire performance of AAEAP,with no weaknesses in its evaluation indexes.They are substantially superior to the means of indexes obtained by other algorithms and close to the maximum values.

    Figure 7:Overall performances of AAEAP on segment and glass data sets

    Table 4 shows the findings on Banana and Phoneme data sets,where AAEAP does not converge to the real category numbers and exhibits a lack of aggregation.Therefore,we also delineate the clustering evaluation findings whenK-AP,SC,and DP converge to the real category numbers.

    Table 4:The clustering results on Banana and Phoneme data sets

    The deviation between the numbers of clusters obtained by AP,CSAP,QPAP,TMPAP,and the real category numbers is significant,and the problem of insufficient aggregation of these four algorithms is obvious.AAEAP converges to the numbers of clusters different from the real numbers since FCM does not offer more mutually exclusive exemplars.However,AAEAP still shows strong automatic aggregation ability even for large data sets compared with AP,CSAP,QPAP,and TMPAP.The performance of ADDAP is superior to that of most algorithms since the density of data is considered and the optimal classification is obtained through iteration.The density distribution is distinctly nonuniform for Banana and Phoneme data sets,enabling ADDAP to offer full play to the superiority.The index values ofK-AP,SC,and DP are mostly inferior to those of AAEAP,regardless of whether they obtain the real category numbers or the same number of clusters as AAEAP.Fig.8 demonstrates the overall performance of AAEAP.The evaluation indexes of AAEAP are superior to the means of indexes obtained by other algorithms and close to the maximum values.

    5 Conclusions

    This study proposes an AAEAP clustering algorithm according to mutually exclusive exemplar processing.Its main objective is to enable AP clustering to automatically aggregate,and the information that enhances aggregation comes from real-time partitioning clustering results,rather than prior knowledge or human intervention.This is also the distinction between the AAEAP algorithm and semi-supervised AP clustering algorithms.Potential mutually exclusive exemplar pairs are identified by cross-checking the partitioning and AP clustering findings.Based on them,the current clusters are disassembled and clustering is restarted based on the mutual exclusion and aggregation constraint,achieving aggregation incrementally.From the experimental findings,it can be discerned that the automatic aggregation impact of AAEAP is substantial,and the entire clustering evaluation index values are superior.However,we also discover that the quality of clustering results is related to whether partitioning clustering can offer stable and reliable mutual exclusion detection information.The more accurate the information,the better the AAEAP clustering impact.Future studies will concentrate on enhancing AAEAP to make it more adaptable to cluster on nonspherical data sets.

    Acknowledgement:The authors wish to acknowledge the contribution of the Beijing Institute of Tracking and Telemetry Technology,China.The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers.

    Funding Statement:This research was supported by Research Team Development Funds of L.Xue and Z.H.Ouyang,Electronic Countermeasure Institute,National University of Defense Technology.

    Author Contributions:Study conception and design:Z.H.Ouyang,L.Xue;data collection:Y.S.Duan;analysis and interpretation of results: Z.H.Ouyang,F.Ding;draft manuscript preparation: Z.H.Ouyang,Y.S.Duan.All authors reviewed the results and approved the final version of the manuscript.

    Availability of Data and Materials:Data will be made available on request.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    日韩成人伦理影院| 女性被躁到高潮视频| 人人妻人人爽人人添夜夜欢视频| 久久这里有精品视频免费| 老女人水多毛片| 免费黄频网站在线观看国产| 精品视频人人做人人爽| 免费女性裸体啪啪无遮挡网站| 五月伊人婷婷丁香| 国产精品女同一区二区软件| 亚洲国产色片| 国产又爽黄色视频| av.在线天堂| 亚洲精品,欧美精品| 自拍欧美九色日韩亚洲蝌蚪91| 国产精品久久久久久av不卡| 女的被弄到高潮叫床怎么办| 亚洲,欧美,日韩| 亚洲精品日韩在线中文字幕| 免费av中文字幕在线| a级毛片黄视频| 老女人水多毛片| 亚洲成人av在线免费| 观看av在线不卡| 欧美日韩视频高清一区二区三区二| 久久精品夜色国产| 秋霞伦理黄片| 成人毛片a级毛片在线播放| 女人精品久久久久毛片| av国产久精品久网站免费入址| 久久久久久久国产电影| 日本黄色日本黄色录像| 制服诱惑二区| 黑人欧美特级aaaaaa片| 秋霞在线观看毛片| 国产不卡av网站在线观看| 久久久久久久久久久久大奶| 高清av免费在线| 久久国内精品自在自线图片| av在线app专区| 色94色欧美一区二区| 少妇被粗大猛烈的视频| 久久99热这里只频精品6学生| 三级国产精品片| 久久久精品免费免费高清| 天天操日日干夜夜撸| av片东京热男人的天堂| 好男人视频免费观看在线| 狠狠精品人妻久久久久久综合| 国产在线视频一区二区| 免费观看a级毛片全部| 黄片播放在线免费| 欧美 日韩 精品 国产| 亚洲国产精品999| 亚洲第一区二区三区不卡| 97在线人人人人妻| 久久久久久久精品精品| 国产亚洲精品久久久com| 蜜桃国产av成人99| 国产成人精品一,二区| 日韩熟女老妇一区二区性免费视频| 天天躁夜夜躁狠狠久久av| 成年美女黄网站色视频大全免费| 精品福利永久在线观看| 边亲边吃奶的免费视频| 国产有黄有色有爽视频| 男女下面插进去视频免费观看 | 成年美女黄网站色视频大全免费| 中文欧美无线码| 日韩av不卡免费在线播放| 91国产中文字幕| 我的女老师完整版在线观看| 欧美精品国产亚洲| 伦理电影免费视频| 日韩成人av中文字幕在线观看| 最近2019中文字幕mv第一页| 久久人妻熟女aⅴ| 22中文网久久字幕| 欧美精品一区二区免费开放| 观看av在线不卡| 亚洲av男天堂| 国产成人精品婷婷| 国产亚洲最大av| 在线观看免费高清a一片| 美女国产高潮福利片在线看| a 毛片基地| 国产黄色视频一区二区在线观看| 中文字幕亚洲精品专区| 精品视频人人做人人爽| 久久婷婷青草| 人人妻人人爽人人添夜夜欢视频| 欧美+日韩+精品| 国产高清不卡午夜福利| 少妇高潮的动态图| 男女无遮挡免费网站观看| 日韩精品免费视频一区二区三区 | 久久99精品国语久久久| av免费在线看不卡| 99香蕉大伊视频| 日韩av在线免费看完整版不卡| 国产精品99久久99久久久不卡 | 国产精品国产三级国产av玫瑰| 黑人高潮一二区| 丰满迷人的少妇在线观看| 亚洲高清免费不卡视频| 性色avwww在线观看| 热re99久久国产66热| 人妻少妇偷人精品九色| 激情视频va一区二区三区| 免费黄频网站在线观看国产| h视频一区二区三区| 女性生殖器流出的白浆| 免费高清在线观看视频在线观看| 99久久中文字幕三级久久日本| 美女xxoo啪啪120秒动态图| 欧美国产精品va在线观看不卡| 国产无遮挡羞羞视频在线观看| 亚洲第一av免费看| 高清视频免费观看一区二区| 国产白丝娇喘喷水9色精品| 日韩av不卡免费在线播放| 我的女老师完整版在线观看| 中文字幕最新亚洲高清| 王馨瑶露胸无遮挡在线观看| 国产乱来视频区| 午夜影院在线不卡| 国产欧美亚洲国产| 肉色欧美久久久久久久蜜桃| 一区二区三区精品91| 日本vs欧美在线观看视频| 综合色丁香网| 女性被躁到高潮视频| 在线亚洲精品国产二区图片欧美| 丁香六月天网| 亚洲成av片中文字幕在线观看 | 中国美白少妇内射xxxbb| 熟女电影av网| 国产精品国产三级专区第一集| 亚洲国产精品专区欧美| 亚洲一级一片aⅴ在线观看| 亚洲国产精品一区三区| 午夜福利视频精品| 在线免费观看不下载黄p国产| 女的被弄到高潮叫床怎么办| 18禁观看日本| 亚洲精品国产av蜜桃| 色婷婷久久久亚洲欧美| 成人二区视频| 蜜桃在线观看..| 美女脱内裤让男人舔精品视频| 大话2 男鬼变身卡| 欧美人与性动交α欧美精品济南到 | 亚洲婷婷狠狠爱综合网| 婷婷色av中文字幕| 考比视频在线观看| 五月开心婷婷网| 91aial.com中文字幕在线观看| 日韩av免费高清视频| 人人妻人人添人人爽欧美一区卜| 51国产日韩欧美| 国产69精品久久久久777片| 久久毛片免费看一区二区三区| 亚洲av欧美aⅴ国产| 我的女老师完整版在线观看| 在线观看免费高清a一片| 国产乱来视频区| 午夜免费观看性视频| 久久久久久久久久人人人人人人| 日韩av不卡免费在线播放| 一级片'在线观看视频| av国产精品久久久久影院| 少妇 在线观看| 一二三四在线观看免费中文在 | 大话2 男鬼变身卡| 搡女人真爽免费视频火全软件| 中国国产av一级| 亚洲欧美中文字幕日韩二区| 晚上一个人看的免费电影| 久久精品国产亚洲av涩爱| 亚洲精品日韩在线中文字幕| 大片电影免费在线观看免费| 国产精品久久久久成人av| 久久毛片免费看一区二区三区| 美女脱内裤让男人舔精品视频| 91国产中文字幕| 国产欧美另类精品又又久久亚洲欧美| 成人无遮挡网站| 妹子高潮喷水视频| 在线亚洲精品国产二区图片欧美| 久久精品aⅴ一区二区三区四区 | 99久久人妻综合| 国产伦理片在线播放av一区| 在线天堂最新版资源| 1024视频免费在线观看| 久久久国产一区二区| 国内精品宾馆在线| 高清在线视频一区二区三区| 免费人成在线观看视频色| 天天躁夜夜躁狠狠久久av| 亚洲欧洲日产国产| 日本欧美国产在线视频| 三级国产精品片| 日韩一区二区视频免费看| 国产成人精品久久久久久| 母亲3免费完整高清在线观看 | 日韩大片免费观看网站| 久久国内精品自在自线图片| 极品少妇高潮喷水抽搐| 亚洲丝袜综合中文字幕| 亚洲欧美精品自产自拍| 色网站视频免费| 日韩不卡一区二区三区视频在线| 青青草视频在线视频观看| 97精品久久久久久久久久精品| 乱码一卡2卡4卡精品| 亚洲人成网站在线观看播放| 国产一区二区三区综合在线观看 | 激情视频va一区二区三区| 亚洲精品国产av蜜桃| 久久久久久久久久人人人人人人| 我的女老师完整版在线观看| 在线观看美女被高潮喷水网站| 亚洲精品日本国产第一区| 一个人免费看片子| 精品国产露脸久久av麻豆| 国产黄色免费在线视频| 日韩三级伦理在线观看| 91精品国产国语对白视频| 丝袜脚勾引网站| a级片在线免费高清观看视频| 亚洲成av片中文字幕在线观看 | 欧美激情 高清一区二区三区| 亚洲在久久综合| 女人久久www免费人成看片| 九九在线视频观看精品| 大陆偷拍与自拍| 最近最新中文字幕大全免费视频 | 国产精品久久久久久精品电影小说| 国产亚洲精品第一综合不卡 | 99热网站在线观看| 最近中文字幕高清免费大全6| 婷婷色av中文字幕| 国产在线视频一区二区| 亚洲色图 男人天堂 中文字幕 | 狠狠精品人妻久久久久久综合| 90打野战视频偷拍视频| 亚洲国产精品专区欧美| 国内精品宾馆在线| 国产色婷婷99| 精品人妻偷拍中文字幕| 国产日韩一区二区三区精品不卡| 青春草国产在线视频| 国产不卡av网站在线观看| 国产 一区精品| 国产亚洲精品久久久com| 成年人免费黄色播放视频| 国国产精品蜜臀av免费| 在线观看人妻少妇| 婷婷色综合大香蕉| 亚洲精品成人av观看孕妇| 国产av码专区亚洲av| 日本av手机在线免费观看| h视频一区二区三区| 极品人妻少妇av视频| 国产日韩欧美视频二区| 日本黄大片高清| 中文字幕av电影在线播放| 久久久欧美国产精品| 少妇的丰满在线观看| 中文字幕制服av| 母亲3免费完整高清在线观看 | a 毛片基地| 欧美日韩综合久久久久久| 久久热在线av| 日韩一区二区视频免费看| 七月丁香在线播放| 午夜精品国产一区二区电影| 欧美精品一区二区免费开放| 少妇 在线观看| 成年美女黄网站色视频大全免费| 精品国产国语对白av| 亚洲性久久影院| 一级片'在线观看视频| 日日啪夜夜爽| 精品国产乱码久久久久久小说| 久久精品夜色国产| 国产成人精品久久久久久| 精品少妇内射三级| 欧美激情极品国产一区二区三区 | 90打野战视频偷拍视频| 国精品久久久久久国模美| 亚洲人成网站在线观看播放| 国产男人的电影天堂91| 国产色爽女视频免费观看| 精品国产乱码久久久久久小说| 人妻系列 视频| 欧美精品一区二区大全| www日本在线高清视频| 亚洲人成77777在线视频| 日本爱情动作片www.在线观看| 国产av一区二区精品久久| 亚洲一级一片aⅴ在线观看| 内地一区二区视频在线| 亚洲天堂av无毛| 久久久久国产精品人妻一区二区| 成人毛片60女人毛片免费| 好男人视频免费观看在线| 欧美日本中文国产一区发布| 在线观看美女被高潮喷水网站| 两个人免费观看高清视频| 精品久久蜜臀av无| 丝袜脚勾引网站| 日本-黄色视频高清免费观看| 精品卡一卡二卡四卡免费| 在线天堂中文资源库| 亚洲国产精品一区三区| 日韩在线高清观看一区二区三区| 国产亚洲最大av| 午夜福利乱码中文字幕| 777米奇影视久久| 精品亚洲成国产av| 一级毛片 在线播放| 三上悠亚av全集在线观看| 国产一区二区三区av在线| 日韩一区二区三区影片| 最新的欧美精品一区二区| 男人爽女人下面视频在线观看| 精品国产一区二区三区久久久樱花| 黄色毛片三级朝国网站| 国产探花极品一区二区| 久久久久久久精品精品| 久久精品国产亚洲av涩爱| 丰满迷人的少妇在线观看| 国产xxxxx性猛交| 国产成人免费观看mmmm| 亚洲图色成人| 少妇人妻久久综合中文| 极品少妇高潮喷水抽搐| 日韩大片免费观看网站| 国产一区有黄有色的免费视频| 色婷婷av一区二区三区视频| 国产日韩欧美亚洲二区| 少妇人妻久久综合中文| 涩涩av久久男人的天堂| 亚洲av.av天堂| 亚洲美女搞黄在线观看| 国产国语露脸激情在线看| 婷婷成人精品国产| 日本色播在线视频| 美女视频免费永久观看网站| 少妇被粗大猛烈的视频| 九九爱精品视频在线观看| 五月伊人婷婷丁香| 国产爽快片一区二区三区| 波多野结衣一区麻豆| 另类精品久久| 波多野结衣一区麻豆| 亚洲精品456在线播放app| 如日韩欧美国产精品一区二区三区| 麻豆乱淫一区二区| 91aial.com中文字幕在线观看| 欧美成人午夜精品| 欧美精品一区二区免费开放| 如何舔出高潮| 久久精品国产自在天天线| 亚洲精品456在线播放app| 国产精品无大码| 99热这里只有是精品在线观看| 欧美精品人与动牲交sv欧美| 亚洲人与动物交配视频| 免费高清在线观看视频在线观看| 国产无遮挡羞羞视频在线观看| 国产黄频视频在线观看| 黄网站色视频无遮挡免费观看| 寂寞人妻少妇视频99o| 国产精品嫩草影院av在线观看| 大话2 男鬼变身卡| 亚洲欧美一区二区三区国产| 国产精品国产三级专区第一集| 欧美成人午夜精品| 波多野结衣一区麻豆| 女人被躁到高潮嗷嗷叫费观| 精品国产一区二区久久| 日日啪夜夜爽| 亚洲国产欧美在线一区| 免费人成在线观看视频色| 午夜久久久在线观看| 久久久精品免费免费高清| www.熟女人妻精品国产 | 国精品久久久久久国模美| 国产欧美亚洲国产| 午夜91福利影院| 香蕉丝袜av| 国产在视频线精品| 男的添女的下面高潮视频| 最近2019中文字幕mv第一页| 国产精品偷伦视频观看了| 在线观看www视频免费| 91国产中文字幕| 国产一级毛片在线| 久久这里只有精品19| 国产日韩欧美亚洲二区| 国产男人的电影天堂91| 另类亚洲欧美激情| 校园人妻丝袜中文字幕| 人人妻人人澡人人爽人人夜夜| av天堂久久9| 国产淫语在线视频| 午夜日本视频在线| 亚洲,一卡二卡三卡| 亚洲欧美色中文字幕在线| 男女啪啪激烈高潮av片| 久久影院123| 久久精品国产亚洲av天美| 亚洲一区二区三区欧美精品| 国产综合精华液| 免费大片18禁| 中文字幕亚洲精品专区| 日韩一本色道免费dvd| 午夜福利影视在线免费观看| 少妇的逼水好多| 日韩制服骚丝袜av| 侵犯人妻中文字幕一二三四区| 欧美变态另类bdsm刘玥| 久久久久久伊人网av| 中文字幕亚洲精品专区| 欧美日韩成人在线一区二区| 国产老妇伦熟女老妇高清| 成人亚洲精品一区在线观看| 两个人看的免费小视频| 国产淫语在线视频| 中国三级夫妇交换| 黑人巨大精品欧美一区二区蜜桃 | 精品少妇内射三级| 亚洲久久久国产精品| 一边亲一边摸免费视频| 精品人妻一区二区三区麻豆| 少妇被粗大猛烈的视频| 韩国高清视频一区二区三区| 亚洲精品aⅴ在线观看| 国产免费视频播放在线视频| 成人亚洲欧美一区二区av| 国产精品嫩草影院av在线观看| 满18在线观看网站| 中文乱码字字幕精品一区二区三区| 日韩av不卡免费在线播放| 超碰97精品在线观看| 欧美bdsm另类| 国产亚洲精品第一综合不卡 | 十八禁网站网址无遮挡| 毛片一级片免费看久久久久| 最近最新中文字幕大全免费视频 | 少妇人妻 视频| 少妇精品久久久久久久| √禁漫天堂资源中文www| 少妇被粗大的猛进出69影院 | 国产又色又爽无遮挡免| 亚洲精品久久午夜乱码| 人体艺术视频欧美日本| 国产精品一区www在线观看| 久久99蜜桃精品久久| 男人舔女人的私密视频| 国产成人91sexporn| 日韩电影二区| 国产精品女同一区二区软件| 老司机影院毛片| 亚洲国产色片| 成人黄色视频免费在线看| 亚洲少妇的诱惑av| 丰满乱子伦码专区| 亚洲精品美女久久av网站| 一级毛片我不卡| 一级片免费观看大全| 成人二区视频| 18禁裸乳无遮挡动漫免费视频| 精品人妻在线不人妻| av天堂久久9| 日韩熟女老妇一区二区性免费视频| 国产精品久久久久成人av| 亚洲av中文av极速乱| 中文字幕制服av| 日韩中文字幕视频在线看片| 亚洲av电影在线观看一区二区三区| 精品人妻熟女毛片av久久网站| 成人无遮挡网站| 国产永久视频网站| 狠狠精品人妻久久久久久综合| 黑人高潮一二区| 国产精品不卡视频一区二区| 美女福利国产在线| 国产成人精品无人区| 黄网站色视频无遮挡免费观看| 搡女人真爽免费视频火全软件| 亚洲国产看品久久| 青青草视频在线视频观看| 黑丝袜美女国产一区| 亚洲欧美一区二区三区国产| 最新中文字幕久久久久| 国产又爽黄色视频| 校园人妻丝袜中文字幕| 天堂中文最新版在线下载| 黄网站色视频无遮挡免费观看| 亚洲国产av新网站| 午夜福利乱码中文字幕| 久久 成人 亚洲| 亚洲第一区二区三区不卡| 青春草国产在线视频| 精品一区二区免费观看| 国产又色又爽无遮挡免| av国产久精品久网站免费入址| 天天躁夜夜躁狠狠久久av| 亚洲精品国产av蜜桃| 一级片'在线观看视频| 精品人妻熟女毛片av久久网站| 亚洲婷婷狠狠爱综合网| 一级片免费观看大全| 亚洲精品国产av蜜桃| 高清av免费在线| 日韩,欧美,国产一区二区三区| 午夜福利影视在线免费观看| 大香蕉久久成人网| 国产亚洲av片在线观看秒播厂| 中文字幕最新亚洲高清| 最近中文字幕2019免费版| 韩国av在线不卡| 搡女人真爽免费视频火全软件| 日韩一区二区视频免费看| 一二三四中文在线观看免费高清| 久久99一区二区三区| 亚洲国产欧美日韩在线播放| 亚洲精品色激情综合| 亚洲久久久国产精品| 2021少妇久久久久久久久久久| 永久网站在线| 如何舔出高潮| 国产男女内射视频| 久久久久久久久久久免费av| 51国产日韩欧美| 亚洲国产av影院在线观看| 国产精品国产av在线观看| 看免费av毛片| 交换朋友夫妻互换小说| 久久久久久人人人人人| 色哟哟·www| 午夜福利,免费看| 亚洲 欧美一区二区三区| 久久久久久久久久人人人人人人| 欧美 亚洲 国产 日韩一| 纵有疾风起免费观看全集完整版| 国产日韩欧美视频二区| 免费播放大片免费观看视频在线观看| 午夜福利视频在线观看免费| 日韩av免费高清视频| 日韩欧美一区视频在线观看| 国产精品一区二区在线不卡| 久久亚洲国产成人精品v| 天美传媒精品一区二区| 女性生殖器流出的白浆| 精品久久久精品久久久| 成人毛片60女人毛片免费| 最近的中文字幕免费完整| 欧美亚洲 丝袜 人妻 在线| 性色av一级| 日韩精品有码人妻一区| 丁香六月天网| 有码 亚洲区| 亚洲欧美成人精品一区二区| 黄片播放在线免费| 性高湖久久久久久久久免费观看| 一级毛片我不卡| 国产成人精品无人区| 少妇熟女欧美另类| 香蕉国产在线看| 男人舔女人的私密视频| 日韩大片免费观看网站| 久久婷婷青草| 久久综合国产亚洲精品| 色94色欧美一区二区| 欧美 亚洲 国产 日韩一| 一级,二级,三级黄色视频| 国产无遮挡羞羞视频在线观看| 久久精品久久久久久噜噜老黄| 精品人妻一区二区三区麻豆| 中文字幕最新亚洲高清| 天堂中文最新版在线下载| 中文字幕制服av| 亚洲精品国产色婷婷电影| 99精国产麻豆久久婷婷| 999精品在线视频| 国产精品三级大全| 99精国产麻豆久久婷婷| 999精品在线视频| √禁漫天堂资源中文www| 人体艺术视频欧美日本| 国产精品久久久久久久久免| 一级毛片电影观看| 欧美日韩一区二区视频在线观看视频在线| 香蕉精品网在线| 男女高潮啪啪啪动态图| 人体艺术视频欧美日本| 黄色 视频免费看| 国产精品免费大片| 最近中文字幕2019免费版| 黄色 视频免费看| 久久久精品94久久精品| 亚洲精品美女久久久久99蜜臀 | 一本—道久久a久久精品蜜桃钙片| 51国产日韩欧美| 国产极品粉嫩免费观看在线| 极品少妇高潮喷水抽搐| 免费久久久久久久精品成人欧美视频 | 人妻 亚洲 视频| 亚洲av.av天堂| 国产精品国产av在线观看| 热re99久久国产66热|