Survey on Different Enhanced K-Means Clustering Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2015 by IJETT Journal
Volume-27 Number-4
Year of Publication : 2015
Authors : Kapil Joshi, Himanshu Gupta, Prashant Chaudhary, Punit Sharma
DOI :  10.14445/22315381/IJETT-V27P233


Kapil Joshi, Himanshu Gupta, Prashant Chaudhary, Punit Sharma "Survey on Different Enhanced K-Means Clustering Algorithm", International Journal of Engineering Trends and Technology (IJETT), V27(4),178-182 September 2015. ISSN:2231-5381. published by seventh sense research group

Data Mining is justify technique used to extract ,meaningful information from mountain of data and Clustering is an important task in Data Mining process which can be used for the purpose to make groups or clusters of the particular given data set which is based on the similarity between them. K-Means clustering is a clustering procedure in which the given data set is divided into K i.e number of clusters. The impact factor of k-means is its simplicity, high efficiency and scalability. However, is also comprises of number of limitations: random selection of initial centroids, number of cluster K need to be initialized and influence by outliers. In view of these deficiencies, this paper represents a survey of improvements done to traditional k-means to handle such limitations and we will compare K-means clustering algorithm with various clustering algorithm.


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Data Mining, Clustering, K-means algorithm, Fuzzy C-means algorithm, Genetic algorithm, Genetic algorithm-K-Means (GAKM).