DocumentCode
1571427
Title
Entropy Based Clustering of Data Streams with Mixed Numeric and Categorical Values
Author
Wang, Shuyun ; Fan, Yingjie ; Zhang, Chenghong ; Xu, Hexiang ; Hao, Xiulan ; Hu, Yunfa
Author_Institution
Dept. of Computingand Inf. Technol., Fudan Univ., Shanghai
fYear
2008
Firstpage
140
Lastpage
145
Abstract
In is paper, a novel algorithm for clustering data streams with mixed numeric and categorical attributes (CNC-Stream)is proposed. A new similarity measure based on entropy determining the similarity between the objects(data points in the stream or the micro- clusters in memory) is also presented here, which makes CNC-Stream work, the experiments conducted on the real data sets and synthetic data sets show that the proposed method is of high quality.
Keywords
data handling; pattern clustering; categorical attributes; entropy based data streams clustering; numeric attributes; Clustering algorithms; Conference management; Data analysis; Entropy; Information science; Information technology; Monitoring; Probability distribution; Random variables; Technology management; Cluster; Data Stream; Entropy; Mix Attributes;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Information Science, 2008. ICIS 08. Seventh IEEE/ACIS International Conference on
Conference_Location
Portland, OR
Print_ISBN
978-0-7695-3131-1
Type
conf
DOI
10.1109/ICIS.2008.57
Filename
4529811
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