Shape and Centroid Independent Clustring Algorithm for Crowd Management Applications

Clustering techniques play an important role in data mining and its related applications. Among the challenging applications that require robust and real-time processing are crowd management and group trajectory applications. In this paper, a robust and low-complexity clustering algorithm is proposed. It is capable of processing data in a manner that is shape and centroid independent. The algorithm is of low complexity due to the novel technique to compute the matrix power. The algorithm was tested on real and synthetic data and test results are reported. Keywords—clustering; data mining; crowd management; group trajectory; matrix power