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Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores.
Tong Wang1, Cynthia Rudin1, Daniel Wagner2
11 Massachusetts Institute of Technology , Cambridge, Massachusetts.
Big Data
|July 22, 2016
Summary
This study introduces automated tools to help crime analysts identify crime series by detecting core patterns. The method uses subspace clustering to find similar crimes, aiding predictive policing efforts.
Area of Science:
- Criminology
- Computer Science
- Data Science
Background:
- Identifying crime series is crucial for predictive policing and offender apprehension.
- Current methods for crime series detection are manual and time-consuming.
- Automated tools are needed to assist crime analysts in discovering crime patterns.
Purpose of the Study:
- To develop and present an automated method for discovering crime series from crime databases.
- To assist crime analysts by providing tools for efficient crime pattern detection.
- To leverage the concept of 'cores' within crime series for accurate identification.
Main Methods:
- Proposes a subspace clustering method where the subspace represents the modus operandi (M.O.).
- Involves constructing a similarity graph to link generally similar crimes.
- Utilizes integer linear programming to identify crime 'cores' and merges them to form complete series.
Main Results:
- The proposed method effectively identifies cores of similar crimes, which characterize the M.O. of offenders.
- Successfully merges these cores to reconstruct full crime series.
- Demonstrates the potential for general pattern detection beyond crime series.
Conclusions:
- Automated crime series detection is feasible using subspace clustering and core identification.
- The method's reliance on both general and specific similarity enhances accuracy.
- The approach has broader applications in pattern detection across various domains.
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