Paper Info
Title
Incremental Cluster Updating Using Gaussian Mixture Model.
Abstract
In this paper, we present a new approach for updating clusters incrementally. The proposed incremental approach preserves comprehensive statistical information of the clusters in form of Gaussian Mixture Models (GMM). As each GMM needs the number of Gaussian (component) as an input parameter, we proposed a method to determine the number of components automatically with introducing the concept of core points. In the updating phase, instead of processing each new sample individually, we collect the new incoming samples and cluster them. By employing the concepts of core points and GMMs, we build a number of GMMs for the new samples and we label the new GMMs based on their similarity to the already existing GMMs. To find the similarity among GMMs, we introduce a new modified version of Kullback-Leibler as a distance function. For merging the current GMMs and the new GMMs, we proposed a new merging mechanism in which the closest components in both GMMs are merged to create a new GMM. Since GMM structure is a compact representation of clusters, there is no increase in the time neither in clustering side nor in updating phase. We measured the accuracy of clusters based on different clustering validity metrics (DB, Dunn, SD and purity) and the results show that our algorithm outperforms other incremental clustering algorithms in terms of quality of the final clusters.
Year
DOI
Venue
2015
10.1007/978-3-319-18356-5_23
ADVANCES IN ARTIFICIAL INTELLIGENCE (AI 2015)
Keywords
Field
DocType
Incremental clustering,Gaussian Mixture Model,Stream data clustering
Cluster (physics),Pattern recognition,Computer science,Metric (mathematics),Gaussian,Artificial intelligence,Cluster analysis,Merge (version control),Mixture model
Conference
Volume
ISSN
Citations 
9091
0302-9743
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Elnaz Bigdeli1214.44
Mehdi Mohammadi2109150.02
Bijan Raahemi315522.29
Stan Matwin43025344.20