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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 740Brief Survey of K-Means Clustering Algorithms

Brief Survey of K-Means Clustering Algorithms

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Abstract:

K-means is one of the most widely used algorithms for clustering. Ease of implementation, efficiency, simplicity, and empirical success are the main reasons for its popularity. In actual application, there are some defects in traditional k-means, for example, the value of K need to be specified ahead, initial clustering center is a random choice and so on; this influences the performance of the K-means. In order to overcome these obstacles, many variants of K-means algorithm have appeared. We provide a brief overview of k-means, point out existing problems; summarize major improvements in the determination of clusters number, the initialization of the cluster, the similarity measurement, the sensitivity of noise and outliers and so on. Further study directions of K-means are pointed at last.

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Mechanical, Information and Industrial Engineering

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Periodical:

Applied Mechanics and Materials (Volume 740)

Pages:

624-628

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.740.624

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Online since:

March 2015

Authors:

Hui Ming Liu*, Jin Grong Lu

Keywords:

Cluster Initialization, K-Means, Number of Clusters, Sensitivity, Similarity Measurement

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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