A Law Enforcement for Crime Detection (RRL-PMD): Relative Record Linkage based Pattern Mining Algorithm Using Decision Classifier to Identify Crime Rates
M. Ramzan Begam1, P. Sengottuvelan2
1M. Ramzan Begam, Research Scholar, Bharathiar University, Coimbatore (Tamil Nadu), India.
2Dr. P. Sengottuvelan, Associate Professor and Head, Department of Computer Science, PG Extension Centre, Periyar University, Dharmapuri (Tamil Nadu), India.
Manuscript received on 26 April 2019 | Revised Manuscript received on 03 May 2019 | Manuscript Published on 08 May 2019 | PP: 593-602 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E12070275S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction of crime data become tremendous in automatic resultant process in law enforcement. Due to vast amount of information in crime records are processed automatically through knowledge mining techniques. The term prediction doesn’t make the relational terms to identify correct crime key terms. So the classification analysis based on statistical attribute crime weightage leads more complex to analyze. To solve this problem, we propose a Relative Record linkage based pattern mining algorithm using decision classifier to identify crime rates(RRL-PMD). By analyzing the real terms observed from crime case arein relative sentence format, the sentence case similarity measure predicts the crime key terms to form cluster. The record linkage generalize the frequency of count term measure to reduce the dimensionality make links based on relative closeness measure. The subset classifier make decision to categorize the risk analyzed from crime records. The proposed system produce higher efficiency to reduce the redundancy of complexity level make efficient crime analysis.
Keywords: Crime Analysis, Decision Classifier, Prediction, Cluster, Semantic Analysis, Record Linkage.
Scope of the Article: Pattern Recognition