Comparative analysis of mid-point based and proposed mean based K-Means Clustering Algorithm for Data Mining
Journal Title: International Journal of Computational Engineering and Management IJCEM - Year 2012, Vol 15, Issue 4
Abstract
In the original k-means algorithm the initial centroids are taken just randomly out of the input data set. But this random selection of initial centroids leads the computation of the algorithm into local optima. Each time the end clustering results will come out to be different. This is the limitation which needs to be dealt with in order to make the k-means algorithm more efficient. The mid –point is used as a metric for computing the initial centroids but this algorithm may be suitable for a wide variety of problems but it is not suitable for all kinds of problems. As it concentrates on calculating the mid-point of different subsets of the data set, so it is most suitable to problems where the input data is regularly or uniformly distributed across the space. But in the situations where the input data is irregular or non-uniformly distributed, this algorithm will not produce the appropriate results. This paper presents the mean as the metric for choosing initial centroid and the comparison of both the algorithms.
Authors and Affiliations
Kirti Aggarwal, Neha Aggarwal
Keywords
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