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Journal of Information Technology Review
 

Crime Prediction Patterns using Hybrid K-Means Hierarchical Clustering
Geeta Chhabra, Vasudha Vashisht
Department of Computer Science and Engineering Amity School of Engineering, Amity University Noida – 201313, Uttar Pradesh, India, Department of Computer Science & Engineering Amity School of Engineering Noida, Uttar Pradesh, India
Abstract: Data clustering in data mining has become an increasingly important research area in recent days. The proposed hybrid algorithms k-means hierarchical clustering uses k-mean clustering combined with the hierarchical cluster centres to analyze the crime patterns. The representative points will then construct the hierarchical tree using agglomerative hierarchical clustering algorithm called dendrogram. We have performed experiments to evaluate our approach on crime data available from National Crime Record Bureau, Ministry of Home Affairs, Govt. of India, website. Clustering is used for grouping the similar patterns to identify crime pattern. The experimental result shows that the proposed hybrid algorithm is more effective. This hybrid approach performs better than hierarchical clustering algorithm in terms of accuracy of clusters.
Keywords: Hierarchical Clustering, K-Mean Clustering, Hierarchical K-means Clustering, Dendrogram, Cluster plot, Data Mining, Data Clustering, Algorithm Crime Prediction Patterns using Hybrid K-Means Hierarchical Clustering
DOI:https://doi.org/10.6025/jitr/2020/11/1/1-11
Full_Text   PDF 1.6 MB   Download:   145  times
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