Criminal Pattern Identification Based on Modified K-Means Clustering

astract:

 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.