A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique
Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impract...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/562194 |
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| author | Saeed Aghabozorgi Teh Ying Wah Tutut Herawan Hamid A. Jalab Mohammad Amin Shaygan Alireza Jalali |
| author_facet | Saeed Aghabozorgi Teh Ying Wah Tutut Herawan Hamid A. Jalab Mohammad Amin Shaygan Alireza Jalali |
| author_sort | Saeed Aghabozorgi |
| collection | DOAJ |
| description | Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets. |
| format | Article |
| id | doaj-art-673ff7b1f7e143eb9b64f182a323d764 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-673ff7b1f7e143eb9b64f182a323d7642025-02-03T01:11:24ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/562194562194A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search TechniqueSaeed Aghabozorgi0Teh Ying Wah1Tutut Herawan2Hamid A. Jalab3Mohammad Amin Shaygan4Alireza Jalali5Faculty of Computer Science & Information Technology Building, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Computer Science & Information Technology Building, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Computer Science & Information Technology Building, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Computer Science & Information Technology Building, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Computer Science & Information Technology Building, University of Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Computer Science & Information Technology Building, University of Malaya, 50603 Kuala Lumpur, MalaysiaTime series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.http://dx.doi.org/10.1155/2014/562194 |
| spellingShingle | Saeed Aghabozorgi Teh Ying Wah Tutut Herawan Hamid A. Jalab Mohammad Amin Shaygan Alireza Jalali A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique The Scientific World Journal |
| title | A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique |
| title_full | A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique |
| title_fullStr | A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique |
| title_full_unstemmed | A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique |
| title_short | A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique |
| title_sort | hybrid algorithm for clustering of time series data based on affinity search technique |
| url | http://dx.doi.org/10.1155/2014/562194 |
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