• <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.

    欧美不卡视频在线免费观看| 免费电影在线观看免费观看| 国产精品美女特级片免费视频播放器| 国产熟女欧美一区二区| 国产免费一级a男人的天堂| 中亚洲国语对白在线视频| 亚洲自偷自拍三级| 国内揄拍国产精品人妻在线| 久久99热这里只有精品18| 国产精品综合久久久久久久免费| 免费无遮挡裸体视频| 搞女人的毛片| 日本一本二区三区精品| 直男gayav资源| 一个人观看的视频www高清免费观看| 啪啪无遮挡十八禁网站| 97热精品久久久久久| 日本撒尿小便嘘嘘汇集6| 男女那种视频在线观看| 欧美区成人在线视频| 亚洲自拍偷在线| 天堂√8在线中文| 亚洲精华国产精华精| 在线免费观看不下载黄p国产 | 天堂av国产一区二区熟女人妻| 国产在线精品亚洲第一网站| 97人妻精品一区二区三区麻豆| 精品人妻一区二区三区麻豆 | 欧美日本亚洲视频在线播放| av在线蜜桃| 12—13女人毛片做爰片一| 99久久久亚洲精品蜜臀av| 波野结衣二区三区在线| 日日摸夜夜添夜夜添小说| 亚洲精品乱码久久久v下载方式| 久久久久久久精品吃奶| 午夜亚洲福利在线播放| 国产精品福利在线免费观看| 99在线视频只有这里精品首页| 精品乱码久久久久久99久播| 人妻夜夜爽99麻豆av| 国产黄片美女视频| netflix在线观看网站| 内地一区二区视频在线| 免费看日本二区| 亚洲精品456在线播放app | 欧美不卡视频在线免费观看| 国产白丝娇喘喷水9色精品| 日本熟妇午夜| 少妇熟女aⅴ在线视频| 一个人看视频在线观看www免费| 欧美人与善性xxx| 国产白丝娇喘喷水9色精品| 欧美色视频一区免费| 国产高清有码在线观看视频| 精品免费久久久久久久清纯| 中文字幕熟女人妻在线| 91久久精品国产一区二区三区| 欧美成人一区二区免费高清观看| 国产精品一区www在线观看 | 在线播放无遮挡| 欧美高清成人免费视频www| 又黄又爽又免费观看的视频| 国产老妇女一区| 日韩大尺度精品在线看网址| 色尼玛亚洲综合影院| a级一级毛片免费在线观看| av在线亚洲专区| 99热精品在线国产| 又黄又爽又刺激的免费视频.| 国产av一区在线观看免费| 日本三级黄在线观看| 亚洲精品乱码久久久v下载方式| 亚洲av不卡在线观看| 99热这里只有精品一区| 又爽又黄a免费视频| 精品久久久久久久久久久久久| 99精品在免费线老司机午夜| 国产免费男女视频| 99热精品在线国产| 免费看日本二区| 久久天躁狠狠躁夜夜2o2o| 国产男人的电影天堂91| 人人妻人人澡欧美一区二区| 伦理电影大哥的女人| 黄色配什么色好看| 国产在线精品亚洲第一网站| 欧美精品国产亚洲| 精华霜和精华液先用哪个| 啦啦啦韩国在线观看视频| av国产免费在线观看| 亚洲一区二区三区色噜噜| av福利片在线观看| 黄色一级大片看看| h日本视频在线播放| 国产探花极品一区二区| 又黄又爽又刺激的免费视频.| 18禁黄网站禁片午夜丰满| 啪啪无遮挡十八禁网站| 亚洲aⅴ乱码一区二区在线播放| 两个人视频免费观看高清| 99国产精品一区二区蜜桃av| 国产高清有码在线观看视频| 欧美一区二区亚洲| 综合色av麻豆| 深爱激情五月婷婷| 我要看日韩黄色一级片| 欧美性感艳星| 日本a在线网址| 久久午夜福利片| 免费观看的影片在线观看| 午夜精品在线福利| 麻豆精品久久久久久蜜桃| 女的被弄到高潮叫床怎么办 | 天天一区二区日本电影三级| 亚洲午夜理论影院| 午夜精品一区二区三区免费看| 窝窝影院91人妻| 亚洲欧美日韩东京热| 在线观看66精品国产| 亚洲成人久久性| 波多野结衣高清无吗| 黄片wwwwww| 亚洲精品粉嫩美女一区| 国产国拍精品亚洲av在线观看| 色哟哟·www| 偷拍熟女少妇极品色| 嫩草影院入口| 丝袜美腿在线中文| 成人无遮挡网站| 亚洲av熟女| 91麻豆精品激情在线观看国产| 成人美女网站在线观看视频| 午夜福利视频1000在线观看| 综合色av麻豆| 91av网一区二区| 老熟妇仑乱视频hdxx| 美女高潮的动态| 国产精品,欧美在线| 一a级毛片在线观看| 嫩草影视91久久| 欧美人与善性xxx| 真人一进一出gif抽搐免费| 淫秽高清视频在线观看| www.色视频.com| 国产毛片a区久久久久| 亚洲人成网站高清观看| 亚洲美女搞黄在线观看 | 丰满的人妻完整版| 亚洲精品在线观看二区| 国产精品一区二区三区四区免费观看 | 国产亚洲91精品色在线| 亚洲狠狠婷婷综合久久图片| 久久精品国产清高在天天线| 九九在线视频观看精品| 色视频www国产| 久久久久久久精品吃奶| 少妇猛男粗大的猛烈进出视频 | 午夜爱爱视频在线播放| 在线观看免费视频日本深夜| 噜噜噜噜噜久久久久久91| 小说图片视频综合网站| 欧美日韩国产亚洲二区| 成人特级黄色片久久久久久久| 嫩草影视91久久| 国产不卡一卡二| 欧美一区二区国产精品久久精品| 18禁在线播放成人免费| 99九九线精品视频在线观看视频| 久久久久久久午夜电影| 国产一区二区在线观看日韩| 婷婷精品国产亚洲av在线| 久久精品人妻少妇| 网址你懂的国产日韩在线| 小说图片视频综合网站| 国产精品一区二区三区四区免费观看 | 两性午夜刺激爽爽歪歪视频在线观看| 中文字幕高清在线视频| 久久久精品大字幕| 国产色爽女视频免费观看| 精品人妻一区二区三区麻豆 | 少妇熟女aⅴ在线视频| 国产激情偷乱视频一区二区| 欧美3d第一页| 色精品久久人妻99蜜桃| 男女之事视频高清在线观看| 最近中文字幕高清免费大全6 | 一夜夜www| 一本久久中文字幕| 能在线免费观看的黄片| 亚洲图色成人| 一进一出抽搐动态| 亚洲av免费高清在线观看| 免费观看的影片在线观看| 99久久精品国产国产毛片| 春色校园在线视频观看| 亚洲va在线va天堂va国产| 午夜精品在线福利| 97热精品久久久久久| 99热网站在线观看| 久久中文看片网| 99热只有精品国产| 亚洲av熟女| 精品免费久久久久久久清纯| 长腿黑丝高跟| 久久久久久大精品| 一级毛片久久久久久久久女| 日本a在线网址| 欧美日韩乱码在线| 亚洲av一区综合| 日本爱情动作片www.在线观看 | 综合色av麻豆| 久久久久久九九精品二区国产| 久久久久久久久久成人| 久久久久久久久久成人| 麻豆成人av在线观看| 国内精品美女久久久久久| 亚洲中文字幕一区二区三区有码在线看| 深夜a级毛片| 1000部很黄的大片| 欧美不卡视频在线免费观看| 日韩在线高清观看一区二区三区 | 日本精品一区二区三区蜜桃| 九色成人免费人妻av| 中文字幕熟女人妻在线| 日本免费a在线| 欧美日韩中文字幕国产精品一区二区三区| 亚洲性夜色夜夜综合| 一个人免费在线观看电影| 亚洲黑人精品在线| 国产一区二区三区在线臀色熟女| 欧美日本亚洲视频在线播放| 午夜a级毛片| 亚洲av免费在线观看| 哪里可以看免费的av片| 91久久精品国产一区二区三区| 哪里可以看免费的av片| 我要搜黄色片| 日韩欧美精品免费久久| 亚洲国产色片| 成人二区视频| 在线播放国产精品三级| 99精品在免费线老司机午夜| 国内久久婷婷六月综合欲色啪| 最近最新中文字幕大全电影3| 99热精品在线国产| 老司机午夜福利在线观看视频| 老司机午夜福利在线观看视频| 最近最新中文字幕大全电影3| 国产亚洲精品综合一区在线观看| 国产伦在线观看视频一区| 又黄又爽又免费观看的视频| av天堂中文字幕网| 精华霜和精华液先用哪个| 精品乱码久久久久久99久播| 狂野欧美激情性xxxx在线观看| av视频在线观看入口| 一本久久中文字幕| 午夜老司机福利剧场| 蜜桃久久精品国产亚洲av| 日韩欧美在线乱码| 婷婷亚洲欧美| 99久久久亚洲精品蜜臀av| 成人三级黄色视频| 婷婷六月久久综合丁香| 九九热线精品视视频播放| 免费搜索国产男女视频| 亚洲欧美清纯卡通| 精品免费久久久久久久清纯| 最后的刺客免费高清国语| 亚洲最大成人手机在线| 国产成人av教育| 校园春色视频在线观看| 国产欧美日韩精品一区二区| 搞女人的毛片| 欧美不卡视频在线免费观看| 精品人妻偷拍中文字幕| 少妇丰满av| 亚洲18禁久久av| 观看美女的网站| 婷婷丁香在线五月| 亚洲人成网站在线播| 久久久精品大字幕| 不卡视频在线观看欧美| 亚洲一区二区三区色噜噜| 五月伊人婷婷丁香| 日本精品一区二区三区蜜桃| 久久久精品欧美日韩精品| 美女xxoo啪啪120秒动态图| 午夜福利在线观看吧| 国产真实乱freesex| 99热这里只有精品一区| 国产在线精品亚洲第一网站| 午夜视频国产福利| 国产精品98久久久久久宅男小说| 国产爱豆传媒在线观看| 久久精品夜夜夜夜夜久久蜜豆| 日本黄色视频三级网站网址| 日韩人妻高清精品专区| 午夜福利欧美成人| 村上凉子中文字幕在线| 亚洲精品在线观看二区| 波野结衣二区三区在线| 免费看a级黄色片| 日韩人妻高清精品专区| 自拍偷自拍亚洲精品老妇| 国产精品一及| 久久久久久大精品| 国产黄a三级三级三级人| 久久久久久久精品吃奶| x7x7x7水蜜桃| 在线看三级毛片| 天堂影院成人在线观看| 色综合婷婷激情| 亚洲色图av天堂| 两个人的视频大全免费| 婷婷色综合大香蕉| 99热网站在线观看| 琪琪午夜伦伦电影理论片6080| 久久6这里有精品| 一级黄片播放器| 精品久久久久久久久久久久久| 日本在线视频免费播放| 可以在线观看毛片的网站| 久久国产乱子免费精品| 亚洲精品粉嫩美女一区| 夜夜爽天天搞| 99热6这里只有精品| 久久久久久久久久久丰满 | 亚洲aⅴ乱码一区二区在线播放| 99在线视频只有这里精品首页| 在线看三级毛片| 成年女人毛片免费观看观看9| 一本精品99久久精品77| 免费在线观看成人毛片| 亚洲在线自拍视频| 看免费成人av毛片| 日日啪夜夜撸| 天堂√8在线中文| 精品久久久久久久末码| 又爽又黄无遮挡网站| 人妻丰满熟妇av一区二区三区| 欧美国产日韩亚洲一区| 成人鲁丝片一二三区免费| 高清毛片免费观看视频网站| 精品免费久久久久久久清纯| 琪琪午夜伦伦电影理论片6080| 日本a在线网址| 成年人黄色毛片网站| 国产人妻一区二区三区在| 免费看日本二区| 欧美一级a爱片免费观看看| 精品国内亚洲2022精品成人| 欧美日韩综合久久久久久 | 免费黄网站久久成人精品| 国产精品人妻久久久久久| 成年女人看的毛片在线观看| 精品久久久久久久久亚洲 | 欧美日韩乱码在线| 国产久久久一区二区三区| 欧美日韩国产亚洲二区| 国产高潮美女av| 国产精品一及| 欧美成人性av电影在线观看| 欧美色欧美亚洲另类二区| 精品久久久久久久久久免费视频| 一个人看视频在线观看www免费| 国内少妇人妻偷人精品xxx网站| av天堂在线播放| 久久婷婷人人爽人人干人人爱| 综合色av麻豆| 嫁个100分男人电影在线观看| 免费一级毛片在线播放高清视频| 精品一区二区三区视频在线观看免费| 深夜a级毛片| 中文字幕熟女人妻在线| 毛片女人毛片| 国内久久婷婷六月综合欲色啪| 又黄又爽又免费观看的视频| 午夜福利高清视频| 日本 av在线| 亚洲av日韩精品久久久久久密| 国产精品久久久久久av不卡| 久久6这里有精品| 欧美丝袜亚洲另类 | 国产精品美女特级片免费视频播放器| 乱码一卡2卡4卡精品| 精品不卡国产一区二区三区| 久久香蕉精品热| 成人国产麻豆网| 99国产精品一区二区蜜桃av| 免费看日本二区| 亚洲国产日韩欧美精品在线观看| 久久香蕉精品热| 搡女人真爽免费视频火全软件 | 麻豆国产av国片精品| 免费人成视频x8x8入口观看| 嫩草影院入口| 最新在线观看一区二区三区| 大又大粗又爽又黄少妇毛片口| 亚洲专区国产一区二区| 搡老岳熟女国产| 一级毛片久久久久久久久女| 国产成人av教育| 人人妻人人看人人澡| 久久久久久久久中文| 中文亚洲av片在线观看爽| 一区二区三区激情视频| 在线观看一区二区三区| 欧美性感艳星| 久久午夜亚洲精品久久| 国产视频内射| 啦啦啦观看免费观看视频高清| 日本黄色视频三级网站网址| 欧美日韩精品成人综合77777| 综合色av麻豆| 在线免费十八禁| 亚洲av成人精品一区久久| 成人一区二区视频在线观看| 亚洲午夜理论影院| 精品一区二区三区视频在线观看免费| 91午夜精品亚洲一区二区三区 | 国内揄拍国产精品人妻在线| 免费人成视频x8x8入口观看| 亚洲男人的天堂狠狠| 久久久久免费精品人妻一区二区| 亚洲精品亚洲一区二区| 久久99热这里只有精品18| 免费大片18禁| 成人av在线播放网站| 成人二区视频| 日韩国内少妇激情av| 3wmmmm亚洲av在线观看| 精品久久久噜噜| 国产亚洲精品久久久久久毛片| 一级a爱片免费观看的视频| 国内毛片毛片毛片毛片毛片| 国产精华一区二区三区| 亚洲美女搞黄在线观看 | 搡女人真爽免费视频火全软件 | 1024手机看黄色片| 在线播放国产精品三级| 一个人看的www免费观看视频| 日本黄色视频三级网站网址| 99热网站在线观看| 男女之事视频高清在线观看| 成年免费大片在线观看| 国产精品人妻久久久久久| 亚洲真实伦在线观看| 色哟哟·www| 可以在线观看的亚洲视频| 免费电影在线观看免费观看| a级毛片免费高清观看在线播放| a在线观看视频网站| 欧美日韩亚洲国产一区二区在线观看| 成人国产综合亚洲| 他把我摸到了高潮在线观看| 国产精华一区二区三区| 国产高清三级在线| 成人毛片a级毛片在线播放| a级毛片免费高清观看在线播放| 久久久久性生活片| 搞女人的毛片| 国产精品福利在线免费观看| 国产精品自产拍在线观看55亚洲| 无人区码免费观看不卡| 精品人妻一区二区三区麻豆 | 亚洲人成网站在线播放欧美日韩| 男女做爰动态图高潮gif福利片| 日韩欧美精品免费久久| 日韩欧美在线二视频| 九九在线视频观看精品| 免费高清视频大片| 99久久精品国产国产毛片| 真人一进一出gif抽搐免费| 啦啦啦韩国在线观看视频| 国产三级中文精品| 国产精品精品国产色婷婷| 国产美女午夜福利| 亚洲性久久影院| 亚洲精品在线观看二区| 我要搜黄色片| 直男gayav资源| 亚洲中文字幕一区二区三区有码在线看| 国产精品一及| 禁无遮挡网站| 看片在线看免费视频| 两性午夜刺激爽爽歪歪视频在线观看| 精品人妻偷拍中文字幕| 超碰av人人做人人爽久久| 中国美女看黄片| 国产精品伦人一区二区| 亚洲av二区三区四区| 一进一出好大好爽视频| 亚洲精品久久国产高清桃花| 国产视频内射| 欧美激情国产日韩精品一区| www日本黄色视频网| 最后的刺客免费高清国语| 欧美潮喷喷水| 男人舔奶头视频| 国产精品久久久久久精品电影| 中国美女看黄片| 免费av不卡在线播放| 毛片一级片免费看久久久久 | 亚洲美女黄片视频| 非洲黑人性xxxx精品又粗又长| 大型黄色视频在线免费观看| 国产人妻一区二区三区在| 成人av一区二区三区在线看| 好男人在线观看高清免费视频| 国产熟女欧美一区二区| 久久天躁狠狠躁夜夜2o2o| 婷婷亚洲欧美| 亚洲国产精品成人综合色| 看免费成人av毛片| 一区二区三区四区激情视频 | 精品一区二区三区av网在线观看| 国产一级毛片七仙女欲春2| 日韩欧美三级三区| 亚洲av免费高清在线观看| 久久久久久久亚洲中文字幕| 国内精品美女久久久久久| 五月伊人婷婷丁香| 最近最新免费中文字幕在线| 国产成人a区在线观看| 午夜影院日韩av| 嫁个100分男人电影在线观看| 在线观看66精品国产| 国产国拍精品亚洲av在线观看| 啦啦啦啦在线视频资源| 亚洲中文字幕日韩| a在线观看视频网站| 亚洲午夜理论影院| 丰满的人妻完整版| 免费在线观看成人毛片| 国产伦在线观看视频一区| 欧美+亚洲+日韩+国产| 99国产精品一区二区蜜桃av| 国内精品久久久久精免费| 亚洲av中文av极速乱 | 亚洲国产色片| 日韩国内少妇激情av| 国产精品久久久久久精品电影| 亚洲精品久久国产高清桃花| 三级男女做爰猛烈吃奶摸视频| 日日干狠狠操夜夜爽| 国产午夜精品论理片| av视频在线观看入口| 日本成人三级电影网站| 一本久久中文字幕| 身体一侧抽搐| 熟女人妻精品中文字幕| 国内少妇人妻偷人精品xxx网站| 欧美在线一区亚洲| 看片在线看免费视频| 久久精品夜夜夜夜夜久久蜜豆| 国产真实乱freesex| 国产精品乱码一区二三区的特点| 人人妻人人澡欧美一区二区| 国产成人影院久久av| 日日摸夜夜添夜夜添av毛片 | 男女边吃奶边做爰视频| 老司机午夜福利在线观看视频| 99视频精品全部免费 在线| 成人国产麻豆网| 国产亚洲精品久久久com| 日韩在线高清观看一区二区三区 | 国产亚洲欧美98| 麻豆成人av在线观看| 欧美性猛交黑人性爽| 日本 欧美在线| 看片在线看免费视频| 精华霜和精华液先用哪个| 欧美一区二区亚洲| 十八禁国产超污无遮挡网站| 九九在线视频观看精品| 国产精品一区二区三区四区久久| 亚洲成a人片在线一区二区| 久久精品国产亚洲av涩爱 | av福利片在线观看| 国产成年人精品一区二区| 亚洲欧美日韩高清专用| 国产午夜精品论理片| 国产精品一及| 美女cb高潮喷水在线观看| 91狼人影院| 亚洲国产欧洲综合997久久,| 97超级碰碰碰精品色视频在线观看| 一夜夜www| 精品人妻一区二区三区麻豆 | 国产伦一二天堂av在线观看| 欧美黑人欧美精品刺激| 欧美中文日本在线观看视频| 1000部很黄的大片| 少妇裸体淫交视频免费看高清| 免费人成在线观看视频色| 最新中文字幕久久久久| 最新在线观看一区二区三区| 国产精品99久久久久久久久| 亚洲精品色激情综合| 日本欧美国产在线视频| 1000部很黄的大片| 免费搜索国产男女视频| 特级一级黄色大片| 女生性感内裤真人,穿戴方法视频| 成人性生交大片免费视频hd| 亚洲性久久影院| 亚洲精品粉嫩美女一区| 亚洲,欧美,日韩| 亚洲av日韩精品久久久久久密| 真实男女啪啪啪动态图| 村上凉子中文字幕在线| 午夜精品久久久久久毛片777| 亚洲精品一卡2卡三卡4卡5卡|