An Improved Genetic k-means Algorithm for Optimal Clustering

In the classical k-means algorithm, the value of k must be confirmed in advance. It is difficult to confirm accurately the value of k in reality. This paper proposes an improved genetic k-means algorithm (IGKM) and constructs a fitness function defined as a product of three factors, maximization of...

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Bibliographic Details
Published inSixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) pp. 793 - 797
Main Authors Hai-xiang Guo, Ke-jun Zhu, Si-wei Gao, Ting Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2006
Subjects
Biological cells
Clustering algorithms
Conferences
Data mining
Genetics
Geology
Geoscience
Information technology
Partitioning algorithms
Physics
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Summary:In the classical k-means algorithm, the value of k must be confirmed in advance. It is difficult to confirm accurately the value of k in reality. This paper proposes an improved genetic k-means algorithm (IGKM) and constructs a fitness function defined as a product of three factors, maximization of which ensures the formation of a small number of compact clusters with large separation between at least two clusters. At last, two artificial and three real-life data sets are considered for experiments that compare IGKM with k-means algorithm, GA-based method and genetic k-means algorithm (GKM) by inter-cluster distance (ITD), inner-cluster distance (IND) and rate of separation exactness. The experiments show that IGKM can automatically reach the optimal value of k with high accuracy
ISBN:0769527922
9780769527925
ISSN:2375-9232
2375-9259
DOI:10.1109/ICDMW.2006.30