NDLI: Automatic clustering using particle swarm optimization with various validity indices

Content Provider	IEEE Xplore Digital Library
Author	Chih-Wei Wang Hwang, J.-I.G.
Copyright Year	2012
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Fu Jen Catholic Univ., Taipei, Taiwan (Chih-Wei Wang; Hwang, J.-I.G.)
Abstract	Data clustering partitions a dataset into clusters where each cluster contains similar data. Clustering algorithms usually require users to set the number of clusters, e.g., k-means or fuzzy c-means. However, it is difficult to determine a meaningful number of clusters if users lack prior knowledge of the data. Data clustering may use a validity index to grade the clustering quality. Most validity indices are based on clustering compactness and separation, but other criteria are also used for clustering. Therefore, no individual validity index is applicable to data with different properties. This paper presents a novel dynamic clustering based on particle swarm optimization. The proposed algorithm is compared with other dynamic clustering algorithms based on particle swarm optimization using artificial and real data sets. The experimental results showed that our proposed algorithm not only determines the appropriate number of clusters with correct cluster centers but can also be applied to data with different properties using various validity indices.
Starting Page	1557
Ending Page	1561
File Size	298767
Page Count	5
File Format	PDF
ISBN	9781467311830
e-ISBN	9781467311847
DOI	10.1109/BMEI.2012.6513143
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	clustering algorithm dynamic clustering data clustering particle swarm optimization Validity index
Content Type	Text
Resource Type	Article

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