Dynamic Clustering of Data with Modified K-Means Algorithm

It is shown that how the modified k-mean algorithm will increase the quality of clusters compared to the K-means algorithm and assigns the data point to their appropriate class or cluster more effectively.

Abstract

K-means is a widely used partitional clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation is needed to reveal whether the optimal number of clusters can be found on the run based on the cluster quality measure. This paper presents a modified K- means algorithm with the intension of improving cluster quality and to fix the optimal number of cluster. The K-means algorithm takes number of clusters (K) as input from the user. But in the practical scenario, it is very difficult to fix the number of clusters in advance. The proposed method works for both the cases i.e. for known number of clusters in advance as well as unknown number of clusters. The user has the flexibility either to fix the number of clusters or input the minimum number of clusters required. In the former case it works same as K-means algorithm. In the latter case the algorithm computes the new cluster centers by incrementing the cluster counter by one in each iteration until it satisfies the validity of cluster quality. It is shown that how the modified k-mean algorithm will increase the quality of clusters compared to the K-means algorithm. It assigns the data point to their appropriate class or cluster more effectively.