Name
Abstract | ||
|---|---|---|
It is a well-known fact that some criminals follow perpetual methods of operations known as modi operandi. Modus operandi is a commonly used term to describe the habits in committing crimes. These modi operandi are used in relating criminals to crimes for which the suspects have not yet been recognized. This paper presents the design, implementation and evaluation of a new method to find connections between crimes and criminals using modi operandi. The method involves generating a feature matrix for a particular criminal based on the flow of events of his/her previous convictions. Then, based on the feature matrix, two representative modi operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modi operandi are compared with the flow of events of the crime at hand, in order to generate two other outputs: completeness probability (CP) and deviation probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a score which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and ten other open data sets. In addition, comparison with nine other classification algorithms showed that the proposed method performs competitively with other related methods proving that the performance of the new method is at an acceptable level. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.7717/peerj-cs.65 | PEERJ COMPUTER SCIENCE |
Keywords | Field | DocType |
Modus operandi analysis,Fuzzy inference systems,Binary feature analysis,Classification,Association rule mining | Generalizability theory,Data mining,Biology,Profiling (computer programming),Fuzzy logic,Association rule learning,Suspect,Criminal investigation,Statistical classification,Completeness (statistics) | Journal |
Volume | ISSN | Citations |
2 | 2376-5992 | 1 |
PageRank | References | Authors |
0.35 | 20 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahawaga Arachchige Pathum Chamikara | 1 | 11 | 3.88 |
| Akalanka Galappaththi | 2 | 1 | 0.68 |
| Roshan Dharshana Yapa | 3 | 7 | 1.21 |
| Ruwan D. Nawarathna | 4 | 27 | 3.10 |
| Saluka R. Kodituwakku | 5 | 3 | 2.82 |
| Jagath Gunatilake | 6 | 1 | 0.68 |
| Aththanapola Arachchilage Chathranee Anumitha Jayathilake | 7 | 1 | 0.35 |
| Liwan H. Liyanage | 8 | 36 | 3.39 |