Automated clustering of trajectory data using a particle swarm optimization. (January 2016)

• Record Type: Journal Article
• Authors: Izakian, Zahedeh; Saadi Mesgari, Mohammad; Abraham, Ajith
• Is Part Of: Computers, environment and urban systems. Volume 55(2016)
• Page Start: 55
• Page End: 65
• Publication Date: 2016-01
• Subjects: Fuzzy C-means clustering -- Particle swarm optimization -- Trajectory data -- Discrete cosine transform -- Dynamic time warping distance; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2015.10.009
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Clustering trajectory data discovers and visualizes available structure in movement patterns of mobile objects and has numerous potential applications in traffic control, urban planning, astronomy, and animal science. In this paper, an automated technique for clustering trajectory data using a Particle Swarm Optimization (PSO) approach has been proposed, and Dynamic Time Warping (DTW) distance as one of the most commonly-used distance measures for trajectory data is considered. The proposed technique is able to find (near) optimal number of clusters as well as (near) optimal cluster centers during the clustering process. To reduce the dimensionality of the search space and improve the performance of the proposed method (in terms of a certain performance index), a Discrete Cosine Transform (DCT) representation of cluster centers is considered. The proposed method is able to admit various cluster validity indexes as objective function for optimization. Experimental results over both synthetic and real-world datasets indicate the superiority of the proposed technique to fuzzy C-means, fuzzy K-medoids, and two evolutionary-based clustering techniques proposed in the literature. Highlights: A particle swarm optimization approach for trajectory data clustering is proposed. A dynamic time warping distance function is used as the similarity measure. The proposed method produces optimal number of clusters as well as optimal cluster centers. A discrete cosine transformation of trajectory data has been considered to reduce the problem search space.