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: Min Ji, Fuding Xie, Yu Ping
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:Abstract and Applied Analysis
Online Access:http://dx.doi.org/10.1155/2013/183410
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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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AT fudingxie adynamicfuzzyclusteralgorithmfortimeseries
AT yuping adynamicfuzzyclusteralgorithmfortimeseries
AT minji dynamicfuzzyclusteralgorithmfortimeseries
AT fudingxie dynamicfuzzyclusteralgorithmfortimeseries
AT yuping dynamicfuzzyclusteralgorithmfortimeseries