Application of Spatiotemporal Fuzzy C-Means Clustering for Crime Spot Detection

被引：6

作者：

Ansari, Mohd Yousuf ^{[1
]}

Prakash, Anand ^{[2
]}

Mainuddin ^{[3
]}

机构：

[1] DRDO Def Sci Informat & Documentat Ctr, Delhi 110054, India

[2] DRDO Inst Syst Studies & Anal, Delhi 110054, India

[3] Jamia Millia Islamia, Dept Elect & Commun, Delhi 110025, India

来源：

DEFENCE SCIENCE JOURNAL | 2018年 / 68卷 / 04期

关键词：

Fuzzy clustering; Spatiotemporal data; Crime data; ALGORITHM;

D O I：

10.14429/dsj.68.12518

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The various sources generate large volume of spatiotemporal data of different types including crime events. In order to detect crime spot and predict future events, their analysis is important. Crime events are spatiotemporal in nature; therefore a distance function is defined for spatiotemporal events and is used in Fuzzy C-Means algorithm for crime analysis. This distance function takes care of both spatial and temporal components of spatiotemporal data. We adopt sum of squared error (SSE) approach and Dunn index to measure the quality of clusters. We also perform the experimentation on real world crime data to identify spatiotemporal crime clusters.

引用

页码：374 / 380

页数：7

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