Comparative Study of Clustering Algorithms Used in CounterTerrorism
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 6
Abstract
Abstract: Data mining can be used to model crime detection problems, detect unusual patterns, terroristactivities and fraudulent behaviour. We will look at k-means clustering with some enhancements to aid in theprocess of identification of crime patterns. The k-means algorithm is one of the frequently used clusteringmethod in data mining, due to its performance in clustering massive data sets. The final clustering result of thek-means clustering algorithm greatly depends upon the correctness of the initial centroids, which are selectedrandomly. The original k-means algorithm converges to local minimum, not the global optimum. Manyimprovements were already proposed to improve the performance of the k-means, but most of these requireadditional inputs like threshold values for the number of data points in a set. In this paper a new method isproposed for finding the better initial centroids and to provide an efficient way of assigning the data points tosuitable clusters with reduced time complexity
Authors and Affiliations
Sanjay Dwivedi, MCA, MPhil, , Prabhat Pandey, PhD, DSc. OSD, , Manmohan Singh Tiwari, PhD, , Mohd. Athar Kalam
Keywords
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