A Dynamic Fuzzy Cluster Algorithm for Time Series
This paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamically clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partit...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2013/183410 |
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| _version_ | 1849405175372972032 |
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| author | Min Ji Fuding Xie Yu Ping |
| author_facet | Min Ji Fuding Xie Yu Ping |
| author_sort | Min Ji |
| collection | DOAJ |
| description | This paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamically
clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining. |
| format | Article |
| id | doaj-art-44da22bfe078451b83fbdac27e673050 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-44da22bfe078451b83fbdac27e6730502025-08-20T03:36:45ZengWileyAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/183410183410A Dynamic Fuzzy Cluster Algorithm for Time SeriesMin Ji0Fuding Xie1Yu Ping2School of Computer Science, Liaoning Normal University, Dalian, Liaoning 116081, ChinaSchool of Urban and Environmental Science, Liaoning Normal University, Dalian, Liaoning 116029, ChinaThe School of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaThis paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamically clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.http://dx.doi.org/10.1155/2013/183410 |
| spellingShingle | Min Ji Fuding Xie Yu Ping A Dynamic Fuzzy Cluster Algorithm for Time Series Abstract and Applied Analysis |
| title | A Dynamic Fuzzy Cluster Algorithm for Time Series |
| title_full | A Dynamic Fuzzy Cluster Algorithm for Time Series |
| title_fullStr | A Dynamic Fuzzy Cluster Algorithm for Time Series |
| title_full_unstemmed | A Dynamic Fuzzy Cluster Algorithm for Time Series |
| title_short | A Dynamic Fuzzy Cluster Algorithm for Time Series |
| title_sort | dynamic fuzzy cluster algorithm for time series |
| url | http://dx.doi.org/10.1155/2013/183410 |
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