Comparative Study of Clustering Algorithms Used in Counter Terrorism
Sanjay Dwivedi, Prabhat Pandey, Manmohan Singh Tiwari, Mohd. Athar Kalam · IOSR Journal of Computer Engineering · 2014
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.