Data clustering with modified K-means algorithm

This paper presents a data clustering approach using modified K-Means algorithm based on the improvement of the sensitivity of initial center (seed point) of clusters. This algorithm partitions the whole space into different segments and calculates the frequency of data point in each segment. The segment which shows maximum frequency of data point will have the maximum probability to contain the centroid of cluster. The number of cluster´s centroid (k) will be provided by the user in the same manner like the traditional K-mean algorithm and the number of division will be k*k (`k´ vertically as well as `k´ horizontally). If the highest frequency of data point is same in different segments and the upper bound of segment crosses the threshold `k´ then merging of different segments become mandatory and then take the highest k segment for calculating the initial centroid (seed point) of clusters. In this paper we also define a threshold distance for each cluster´s centroid to compare the distance between data point and cluster´s centroid with this threshold distance through which we can minimize the computational effort during calculation of distance between data point and cluster´s centroid. It is shown that how the modified k-mean algorithm will decrease the complexity & the effort of numerical calculation, maintaining the easiness of implementing the k-mean algorithm. It assigns the data point to their appropriate class or cluster more effectively.