Abstract
Day by day the quantum of data has been increasing not only in terms of user generated content in social media but also outside the social media, due to which the data has gone from scarce to superabundant that conveys new advantages to users. This explosion of data has made it difficult to handle and analyze huge datasets. Therefore, the techniques of Data Mining assist in exploring and analyzing enormous datasets and helps in discovering meaningful patterns. Clustering is one such task of Data Mining that gathers all the data and partitions it into various groups taking into account their similarity or closeness measure. Clustering in the field of Social Science is used in identification, analysis and detection of various crime patterns. This paper proposes the Modified k-means clustering technique which is applied on the fictitious crime data in order to identify various crime patterns or trends and make a variety of predictions from the analysis of different crime patterns.
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Vidyavathi, B.M., Neha, D.: A survey on applications of data mining using clustering techniques. Int. J. Comput. Appl. 126(2), 7–12 (2015). (0975-8887)
Haiyang, Z.: A Short Introduction to Data Mining and Its Applications. IEEE (2011)
Arora, S., Chana, I.: A survey of clustering techniques for big data analysis, IEEE. In: 5th International Conference Confluence The Next Generation Information Technology Summit (Confluence), pp. 59–65 (2014)
Malathi, A., Baboo, S.S.: Evolving data mining algorithms on the prevailing crime Trend—An intelligent crime prediction model. Int. J. Sci. Eng. Res. 2(6), 1–6 (2011)
Malathi, A., Baboo, S.S.: An enhanced algorithm to predict future crime using data mining. Int. J. Comput. Appl. 21(1), 1–6 (2011)
Ramzan Begam, M., Sengottuvelan, P., Ramani, T.: Survey: tools and techniques implemented in crime data sets. IJISET—Int. J. Innov. Sci. Eng. Technol. 2(6), 707–710 (2015)
Agarwal, J., Nagpal, R., Sehgal, R.: Crime analysis using k-means clustering. Int. J. Comput. Appl. 83(4), 1–4 (2013)
Na, S., Xumin, L., yong, G.: Research on k-means Clustering Algorithm-An improved k-means Clustering Algorithm. In: IEEE Third International Symposium on Intelligent Information Technology and Security Informatics, pp. 63–67 (2010)
Zhang, C., Shixiong, X.: k-means clustering algorithm with improved initial center. In: IEEE, Second International Workshop on Knowledge Discovery and Data Mining, pp. 790–792 (2009)
Kumar, Y., Sahoo, G.: A new initialization method to originate initial cluster centres for k-means algorithm. Int. J Adv. Sci. Technol. 62, 43–54 (2014)
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Vidyavathi, B.M., Neha, D. (2018). Prediction of Crime Trends Using Mk-MC Technique. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542 . Springer, Singapore. https://doi.org/10.1007/978-981-10-3223-3_40
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DOI: https://doi.org/10.1007/978-981-10-3223-3_40
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