A comparative study of efficient initialization methods for the k-means clustering algorithm

► K-means is the most widely used partitional clustering algorithm. ► k-means is highly sensitive to the selection of the initial centers. ► We present an overview of k-means initialization methods (IMs). ► We then compare eight commonly used linear time IMs. ► We demonstrate that popular IMs often...

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Vydáno v:Expert systems with applications Ročník 40; číslo 1; s. 200 - 210
Hlavní autoři: Celebi, M. Emre, Kingravi, Hassan A., Vela, Patricio A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier Ltd 01.01.2013
Elsevier
Témata:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Cluster analysis
Cluster center initialization
Clustering
Clusters
Collection
Computational efficiency
Computer science; control theory; systems
Data processing. List processing. Character string processing
Exact sciences and technology
Expert systems
k-means
Memory organisation. Data processing
Partitional clustering
Software
Statistical tests
Sum of squared error criterion
Theoretical computing
k-means
Sum of squared error criterion
Partitional clustering
Cluster center initialization
Initialization
Gradient
Non parametric test
Statistical analysis
Cluster
Linear time
K means algorithm
Recommendation
Search algorithm
Gradient descent
Experimental result
Efficiency
Classification
Descent method
Database
Linear complexity
Alternative method
Occupation time
Time complexity
ISSN:0957-4174, 1873-6793
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Shrnutí:► K-means is the most widely used partitional clustering algorithm. ► k-means is highly sensitive to the selection of the initial centers. ► We present an overview of k-means initialization methods (IMs). ► We then compare eight commonly used linear time IMs. ► We demonstrate that popular IMs often perform poorly. K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of the cluster centers. Numerous initialization methods have been proposed to address this problem. In this paper, we first present an overview of these methods with an emphasis on their computational efficiency. We then compare eight commonly used linear time complexity initialization methods on a large and diverse collection of data sets using various performance criteria. Finally, we analyze the experimental results using non-parametric statistical tests and provide recommendations for practitioners. We demonstrate that popular initialization methods often perform poorly and that there are in fact strong alternatives to these methods.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.07.021