Automated variable weighting in k-means type clustering

This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The co...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 27; no. 5; pp. 657 - 668
Main Authors: Huang, J.Z., Ng, M.K., Hongqiang Rong, Zichen Li
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.2005
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Additives
Algorithms
Applied sciences
Artificial Intelligence
Cluster Analysis
Clustering
Clustering algorithms
Clustering methods
Clusters
Computer science; control theory; systems
Computer Simulation
Cost function
Data mining
Exact sciences and technology
feature evaluation and selection
Index Terms- Clustering
Information Storage and Retrieval - methods
Input variables
Intelligence
Iterative algorithms
Mathematical analysis
mining methods and algorithms
Models, Statistical
Noise reduction
Numerical Analysis, Computer-Assisted
Partitioning algorithms
Pattern analysis
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Recovering
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
mining methods and algorithms
feature evaluation and selection
K means algorithm
Data mining
Clustering
ISSN:0162-8828, 1939-3539
Online Access:Get full text
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Description
Summary:This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2005.95