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Mining frequent patterns of crime using FP-Growth with multiple minimum supports based on Shannon Entropy

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dc.contributor.author Matto, George
dc.contributor.author Mwangoka, Joseph
dc.date.accessioned 2023-03-22T05:53:24Z
dc.date.available 2023-03-22T05:53:24Z
dc.date.issued 2018
dc.identifier.citation Matto, G. and Mwangoka, J. (2018). Mining frequent patterns of crime using FP-Growth with multiple minimum supports based on Shannon Entropy. International Journal of Computer Applications, 180(24), 45-52. en_US
dc.identifier.issn 0975 – 8887
dc.identifier.uri http://repository.mocu.ac.tz/xmlui/handle/123456789/333
dc.description.abstract FP-Growth is one of the most effective and widely used association rules mining algorithm for discovering interesting relations between items in large datasets. Unfortunately, classical FP-Growth mines frequent patterns by using single user-defined minimum support threshold. This is not adequate for real life applications such as crime patterns mining. On one side, if minimum support is set too low, huge amount of crime patterns (including uninteresting patterns) may be generated, and on the other side, if it is set too high lots of interesting patterns (including seasonal patterns) may be lost. This paper proposes the use of Multiple Item Support (MIS) thresholds instead of single minimum support to tackle the challenge. We employ Shannon entropy method to develop an algorithm that obtains MIS values from crime datasets. The proposed approach is tested on different sizes of input data via a developed working prototype. Experimental results show that our suggested approach outperforms classical FP-Growth in terms of running time and memory use. en_US
dc.language.iso en en_US
dc.publisher NM-AIST en_US
dc.relation.ispartofseries 180;24
dc.subject FP-Growth en_US
dc.subject Crime Pattern en_US
dc.subject Multiple Minimum Supports en_US
dc.subject Shannon Entropy en_US
dc.title Mining frequent patterns of crime using FP-Growth with multiple minimum supports based on Shannon Entropy en_US
dc.type Article en_US


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