An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts

doi:10.1109/TKDE.2016.2594068

	An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts
	Bai, Liang 1,2; Cheng, Xueqi 1; Liang, Jiye 2; Shen, Huawei 1
	2016-11-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	28 期号:11 页码:2871-2883
摘要	There is always a lack of a cluster validity function and optimization strategy to find out clusters and catch the evolution trend of cluster structures on a categorical data stream. Therefore, this paper presents an optimization model for clustering categorical data streams. In the model, a cluster validity function is proposed as the objective function to evaluate the effectiveness of the clustering model while each new input data subset is flowing. It simultaneously considers the certainty of the clustering model and the continuity with the last clustering model in the clustering process. An iterative optimization algorithm is proposed to solve an optimal solution of the objective function with some constraints. Furthermore, we strictly derive a detection index for drifting concepts from the optimization model. We propose a detection method that integrates the detection index and the optimization model to catch the evolution trend of cluster structures on a categorical data stream. The new method can effectively avoid ignoring the effect of the clustering validity on the detection result. Finally, using the experimental studies on several real data sets, we illustrate the effectiveness of the proposed algorithm in clustering categorical data streams, compared with existing data-streams clustering algorithms.
关键词	Cluster analysis optimization model iterative algorithm categorical data stream drifting-concept detection
DOI	10.1109/TKDE.2016.2594068
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61305073] ; National Natural Science Foundation of China[61432011] ; National Natural Science Foundation of China[61472400] ; National Natural Science Foundation of China[61573229] ; National Natural Science Foundation of China[U1435212] ; National Key Basic Research and Development Program of China (973)[2013CB329404] ; National Key Basic Research and Development Program of China (973)[2014CB340400] ; Foundation of Doctoral Program Research of Ministry of Education of China[20131401120001] ; Technology Research Development Projects of Shanxi[2015021100] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2014104] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2015107]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000385702000004
出版者	IEEE COMPUTER SOC
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8004
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bai, Liang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan 030006, Shanxi, Peoples R China
推荐引用方式 GB/T 7714	Bai, Liang,Cheng, Xueqi,Liang, Jiye,et al. An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2016,28(11):2871-2883.
APA	Bai, Liang,Cheng, Xueqi,Liang, Jiye,&Shen, Huawei.(2016).An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,28(11),2871-2883.
MLA	Bai, Liang,et al."An Optimization Model for Clustering Categorical Data Streams with Drifting Concepts".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 28.11(2016):2871-2883.