A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIES
Clustering is an unsupervised learning method that 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 computat...
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doaj-45b04634fc114ec087c6680706d3d3ee2020-11-25T01:39:01ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342019-10-01XLII-4-W1873774010.5194/isprs-archives-XLII-4-W18-737-2019A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIESA. Moayedi0R. A. Abbaspour1A. Chehreghan2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranMining Engineering Faculty, Sahand University of Technology, Tabriz, IranClustering is an unsupervised learning method that 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, Grey Wolf Optimizer (GWO), 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, and SCA algorithms show the best results in the clustering regarding the Purity, and computation time metrics, respectively.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/737/2019/isprs-archives-XLII-4-W18-737-2019.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
A. Moayedi R. A. Abbaspour A. Chehreghan |
spellingShingle |
A. Moayedi R. A. Abbaspour A. Chehreghan A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIES The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
A. Moayedi R. A. Abbaspour A. Chehreghan |
author_sort |
A. Moayedi |
title |
A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIES |
title_short |
A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIES |
title_full |
A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIES |
title_fullStr |
A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIES |
title_full_unstemmed |
A COMPARISON OF EFFICIENCY OF THE OPTIMIZATION APPROACH FOR CLUSTERING OF TRAJECTORIES |
title_sort |
comparison of efficiency of the optimization approach for clustering of trajectories |
publisher |
Copernicus Publications |
series |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
1682-1750 2194-9034 |
publishDate |
2019-10-01 |
description |
Clustering is an unsupervised learning method that 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, Grey Wolf Optimizer (GWO), 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, and SCA algorithms show the best results in the clustering regarding the Purity, and computation time metrics, respectively. |
url |
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/737/2019/isprs-archives-XLII-4-W18-737-2019.pdf |
work_keys_str_mv |
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