| A COMPARISON OF THE EFFICIENCY OF OPTIMIZATION ALGORITHMS IN CLUSTERING OF SPATIO-TEMPORAL TRAJECTORIES |
| Paper ID : 1358-SMPR |
| Authors: |
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Ali Moayedi *1, Rahim Ali Abaspour1, Alireza chehreghan2 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran. 2Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran. |
| Abstract: |
| Clustering is an unsupervised learning method used to discover hidden patterns in large sets of data. Huge data volume and the multidimensionality of trajectories have made their clustering a more challenging task. K-means is a widely used clustering algorithm applied in the trajectory computation field. However, the critical issue with this algorithm is its dependency on the initial values and getting stuck in the local minimum. Meta-heuristic algorithms with the goal of minimizing the cost function of the K-means algorithm can be utilized to address this problem. In this paper, after suggesting a cost function, we compare clustering performance of seven known metaheuristic population-based algorithms including Artiļ¬cial Bee Colonies (ABC), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Multi-Verse Optimizer (MVO), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Whale Optimization Algorithm (WOA). The results obtained from the clustering of several data sets with class labels were assessed by internal and external clustering validation indices along with computation time factor. According to the results, PSO, ABC, and SCA algorithms show the best results in the clustering regarding the Purity, Silhouette, and computation time metrics, respectively. |
| Keywords: |
| Trajectories, Clustering, K-means, Optimization, Meta-heuristic algorithms, DTW |
| Status : Conditional Accept (Oral Presentation) |
