Criminal pattern identification based on modified K-means clustering

Aljrees, Turki, Shi, Daming, Windridge, David ORCID logoORCID: and Wong, B. L. William ORCID logoORCID: (2016) Criminal pattern identification based on modified K-means clustering. 2016 International Conference on Machine Learning and Cybernetics (ICMLC). In: 2016 International Conference on Machine Learning and Cybernetics (ICMLC), 10-13 July 2016, Jeju, South Korea. ISBN 9781509003907. ISSN 2160-1348 [Conference or Workshop Item] (doi:10.1109/ICMLC.2016.7872990)


Data mining methods like clustering enable police to get a clearer picture of criminal identification and prediction. Clustering algorithms will help to extracts hidden patterns to identify groups and their similarities. In this paper, a modified k-mean algorithm is proposed. The data point has been allocated to its suitable class or cluster more remarkably. The Modified k-mean algorithm reduces the complex nature of the numerical computation, thereby retaining the effectiveness of applying the k-mean algorithm. Firstly, the data are extracted from the communications and movements record after tracking the park visitors over three days. Then, the original data will be visualised in a graphical format to help make a decision about how many numbers to consider as the K cluster. Secondly, the modified k-means algorithm on the clusters initial centre sensitivity will be performed. This will link similar segments and determine the occurrence of each data point in every segment group rather than partitioning the entire space into various segments and calculating the occurrence of the data point in every segment. Thirdly, result checking and a comparison with the normal k-mean will be performed. The investigation will focus on the movement of people around the park where the crime occurred, and how people move and communicate in the park, how patterns change, and the movement of groups and individuals. The experiments show that the modified K-means algorithm leads to a better way of observing the data to identify groups and their similarities and dissimilarities in the criminal dataset as a specific domain.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23673
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Depositing User: David Windridge
Date Deposited: 27 Feb 2018 15:59
Last Modified: 12 Jun 2023 15:06

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