A Weighted Fuzzy Clustering Algorithm for Data Stream

Mining data streams poses great challenges due to the limited memory availability and real time query response requirement. One of the most important mining tasks is clustering. There already lots of clustering algorithms for data stream have been presented. Fuzzy cluster is an important clustering...

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Bibliographic Details
Published in2008 ISECS International Colloquium on Computing, Communication, Control, and Management Vol. 1; pp. 360 - 364
Main Authors Renxia Wan, Xiaoya Yan, Xiaoke Su
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2008
Subjects
Algorithm design and analysis
cluster
Clustering algorithms
Data mining
data stream
Equations
Gain
Machine learning algorithms
Partitioning algorithms
Weighted Fuzzy C-Mean
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Summary:Mining data streams poses great challenges due to the limited memory availability and real time query response requirement. One of the most important mining tasks is clustering. There already lots of clustering algorithms for data stream have been presented. Fuzzy cluster is an important clustering method. However, to the best of our knowledge, all the clustering algorithms are hard clustering methods, fuzzy clustering algorithm is presently not used directly for data streams. Fuzzy c-means (FCM) is a typical fuzzy clustering algorithm. In this paper, we extend FCM and propose a weighted fuzzy algorithm for clustering data stream. Experimental results on both synthetic and real data sets show its superiority over the traditional FCM algorithms.
ISBN:076953290X
9780769532905
ISSN:2154-9613
DOI:10.1109/CCCM.2008.186