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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Main Authors: A. Moayedi, R. A. Abbaspour, A. Chehreghan
Format: Article
Language:English
Published: Copernicus Publications 2019-10-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W18/737/2019/isprs-archives-XLII-4-W18-737-2019.pdf
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spelling 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
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