Comparative Study of Clustering Algorithms Used in Counter Terrorism

Abstract

Data mining can be used to model crime detection problems, detect unusual patterns, terrorist activities and fraudulent behaviour. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. The k-means algorithm is one of the frequently used clustering method in data mining, due to its performance in clustering massive data sets. The final clustering result of the k-means clustering algorithm greatly depends upon the correctness of the initial centroids, which are selected randomly. The original k-means algorithm converges to local minimum, not the global optimum. Many improvements were already proposed to improve the performance of the k-means, but most of these require additional inputs like threshold values for the number of data points in a set. In this paper a new method is proposed for finding the better initial centroids and to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity.