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      PAKDD 2013(第17屆亞太地區(qū)知識發(fā)現與數據挖掘國際會議)征文通知

      2013-04-18 03:55:52
      計算機工程與科學 2013年2期
      關鍵詞:黃金海岸亞太地區(qū)征文

      黃金海岸,澳大利亞 (Gold Coast,Australia)

      2013年4月14日~2013年4月17日

      http://pakdd2013.pakdd.org/

      Important Dates

      Paper submission due:Oct.1 (Mon).2012 Notification to author:Dec.19 (Wed).2012

      Camera ready due:Jan.6 (Sun).2013

      *[23:59:59Pacific Standard Time]

      Conference Scope

      The Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD)is a leading international conference in the areas of data mining and?knowledge discovery(KDD).It provides an international forum for researchers and industry practitioners to share their new ideas,original research?results and practical development experiences from all KDD related areas,including data mining,data warehousing,machine learning,artificial?intelligence,databases,statistics,knowledge engineering,visualization,and decision-making systems.The conference calls for research papers?reporting original investigation results and industrial papers reporting real data mining applications and system development experience.

      Topics

      The topics of relevance for the conference papers include but not limited to the following:

      ·Novel models and algorithms·Parallel,distributed,and high performance data

      ·Clusteringmining on the cloud platform

      ·Classification·Privacy preserving data mining

      ·Ranking·Mining high dimensional data

      ·Association analysis·Mining uncertain data

      ·Anomaly detection·Mining imbalanced data

      ·Data pre-processing·Mining dynamic/streaming data

      ·Feature extraction and selection·Statistical methods for data mining

      ·Mining heterogeneous data·Visual data mining

      ·Mining multi-source data·Interactive and online mining

      ·Mining sequential data·Mining behavioral data

      ·Mining spatial and temporal data·Mining multimedia data

      ·Mining unstructured and semi-structured data·Mining scientific databases

      ·Mining graph and network data·Ubiquitous knowledge discovery

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