特异群组挖掘：框架与应用

doi:10.11959/j.issn.2096-0271.2015020

Abstract

Abstract:

Abnormal groups can be found in a wide range of areas.Together with clustering and outlier detection,their goals are all to partition a data set according to data similarity.However,abnormal group mining (AGM) is different in problem definition,algorithm design and applications.To the best of our knowledge,the abnormal group mining problem was investigated systematically.The differences among AGM,clustering and outlier detection were analyzed.The formalized definitions on AGM and a framework algorithm were presented,and several interesting applications were particularized.

Key words: big data, data mining, abnormal group, clustering, outlier detection, data similarity

Yun Xiong, Yangyong Zhu. Abnormal Group Mining:Framework and Applications[J]. Big Data Research, 2015, 1(2): 66-77.

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References 26

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