Abnormal Group Mining:Framework and Applications

Yun Xiong; Yangyong Zhu

doi:10.11959/j.issn.2096-0271.2015020

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Abnormal Group Mining:Framework and Applications

Research | 更新时间：2024-06-05

- Abnormal Group Mining:Framework and Applications
- BIG DATA RESEARCH Vol. 1, Issue 2, Pages: 2015020(2015)
- 作者机构：
  
  1. 复旦大学计算机科学技术学院上海 201203
  2. 上海市数据科学重点实验室（复旦大学）上海 201203
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2015020
  CLC：
- Online First：2015-07，
  
  Published：20 July 2015
- 稿件说明：
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Yun Xiong, Yangyong Zhu. Abnormal Group Mining:Framework and Applications[J]. BIG DATA RESEARCH, 2015, 1(2): 2015020.
DOI：

Yun Xiong, Yangyong Zhu. Abnormal Group Mining:Framework and Applications[J]. BIG DATA RESEARCH, 2015, 1(2): 2015020. DOI： 10.11959/j.issn.2096-0271.2015020.

摘要

特异群组挖掘在证券金融、医疗保险、智能交通、社会网络和生命科学研究等领域具有重要应用价值。特异群组挖掘与聚类、异常挖掘都属于根据数据对象的相似性来划分数据集的数据挖掘任务，但是，特异群组挖掘在问题定义、算法设计和应用效果方面不同于聚类和异常等挖掘任务。为此，系统地阐述了特异群组挖掘任务，分析了特异群组挖掘任务与聚类、异常等任务之间的差异，给出了特异群组挖掘任务的形式化描述及其基础算法，最后，列举了特异群组挖掘的几个重点应用。

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.

关键词

Keywords

references

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