Extensions of Kmeans-Type Algorithms: A New Clustering Framework by Integrating Intracluster Compactness and Intercluster Separation

Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well separated. However, most kmeans-type clustering algorithms rely on only intracluster compactness while overlooking intercluster separatio...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 25; no. 8; pp. 1433 - 1446
Main Authors: Huang, Xiaohui, Ye, Yunming, Zhang, Haijun
Format: Journal Article
Language:English
Published: United States IEEE 01.08.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Approximation methods
Clustering
Clustering algorithms
Clusters
data mining
feature weighting
Games
kmeans
Learning
Linear programming
Mathematical analysis
Neural networks
Partitioning algorithms
Separation
Vectors
feature weighting
Clustering
data mining
kmeans
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well separated. However, most kmeans-type clustering algorithms rely on only intracluster compactness while overlooking intercluster separation. In this paper, a series of new clustering algorithms by extending the existing kmeans-type algorithms is proposed by integrating both intracluster compactness and intercluster separation. First, a set of new objective functions for clustering is developed. Based on these objective functions, the corresponding updating rules for the algorithms are then derived analytically. The properties and performances of these algorithms are investigated on several synthetic and real-life data sets. Experimental studies demonstrate that our proposed algorithms outperform the state-of-the-art kmeans-type clustering algorithms with respect to four metrics: accuracy, RandIndex, Fscore, and normal mutual information.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2013.2293795