Shape and Centroid Independent Clustring Algorithm for Crowd Management Applications
This addresses crowd management applications requiring real-time processing, but appears incremental as it focuses on a novel technique for matrix power computation within clustering.
The paper tackled the problem of robust and real-time clustering for crowd management by proposing a shape and centroid independent algorithm with low complexity, achieving processing capabilities as tested on real and synthetic data.
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.