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Information Granulation-Based Fuzzy Clustering of Time Series.
- Source :
-
IEEE transactions on cybernetics [IEEE Trans Cybern] 2021 Dec; Vol. 51 (12), pp. 6253-6261. Date of Electronic Publication: 2021 Dec 22. - Publication Year :
- 2021
-
Abstract
- In this article, we propose a two-stage time-series clustering approach to cluster time series with different shapes. The first step is to represent the time series by a suite of information granules following the principle of justifiable granularity to perform dimensionality reduction, while the second step is to realize the fuzzy clustering of the time series in the transformed representation space (viz., the space of information granules). In the dimensionality reduction process, the numerical data are granulated using a collection of information granules forming a new sequence that can well describe the original time series. Then, when clustering the time series, dynamic time warping (DTW) is employed to measure the similarity between time series and DTW barycenter averaging (DBA) is generalized to weighted DBA to be involved in the fuzzy C -means (FCMs) algorithm. Finally, the experiments are conducted on the datasets coming from UCR time-series database and Chinese stocks to demonstrate the effectiveness and advantages of the proposed fuzzy clustering approach.
- Subjects :
- Cluster Analysis
Databases, Factual
Time Factors
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 2168-2275
- Volume :
- 51
- Issue :
- 12
- Database :
- MEDLINE
- Journal :
- IEEE transactions on cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 32112690
- Full Text :
- https://doi.org/10.1109/TCYB.2020.2970455