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Volume 5 Issue 12

December 2019

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M. Sivamani


Clustering, K-means clustering ,Computational complexity


Data mining is the process of extracting useful information from the large amount of data and converting it into understandable form for further use. Clustering is the process of grouping object attributes and features such that the data objects in one group are more similar than data objects in another group. But it is now very challenging due to the sharply increase in the large volume of data generated by number of applications. K means is a simple and widely used algorithm for clustering data. But, the traditional k-means is computationally expensive; sensitive to outlier’s .Algorithm result in optimal number of cluster Second algorithm reduce computational complexity and remove dead unit problem. It select the most populated area as cluster center. Third algorithm use simple data structure that can be used to store information in each iteration and that information can be used in next iteration. It increase the speed of clustering and reduce time complexity. Solving these Issues is the subject of many recent research works. In this paper, we will do a review on k-means clustering algorithms.

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Published in: Volume : 5, Issue : 8
Publication Date: 8/1/2019

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