Abstract
In this paper, we present an algorithm for cluster detection using modified Watershed model. The presented model for cluster detection works better than the k-means algorithm. The proposed algorithm is also computationally inexpensive compared to the k-means, agglomerative hierarchical clustering and DBSCAN algorithm. The clustering results can be considered as good as the results of DBSCAN and sometimes the result obtained by the proposed model is better than the DBSCAN results. The presented algorithm solves the conflicts faced by the DBSCAN in case of varying density. This paper also presents a way to reduce high dimensional data to low dimensional data with automatic association analysis. This algorithm can reduce high dimensional data to even a single dimension. Using this algorithm the challenges faced in multidimensional clustering by different algorithms such as DBSCSN is solved. This dimensionality reduction with automatic association algorithm is then applied to the Watershed model to detect cluster in Homicide Data and finding out murder prone zones and suggest a person with murder avoiding areas.
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Khisha, J., Zerin, N., Choudhury, D., Rahman, R.M. (2017). Determining Murder Prone Areas Using Modified Watershed Model. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10448. Springer, Cham. https://doi.org/10.1007/978-3-319-67074-4_30
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DOI: https://doi.org/10.1007/978-3-319-67074-4_30
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