Linear Fuzzy Information-Granule-Based Fuzzy C-Means Algorithm for Clustering Time Series

This article aims to design a trend-oriented-granulation-based fuzzy <inline-formula> <tex-math notation="LaTeX">C </tex-math></inline-formula>-means (FCM) algorithm that can cluster a group of time series at an abstract (granular) level. To achieve a better trend-o...

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Vydáno v:IEEE transactions on cybernetics Ročník 53; číslo 12; s. 7622 - 7634
Hlavní autoři: Yang, Zonglin, Jiang, Shurong, Yu, Fusheng, Pedrycz, Witold, Yang, Huilin, Hao, Yadong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Clustering
Clustering algorithms
Dynamic time warping (DTW)
fuzzy C-means (FCM)
Granular materials
Granulation
Heuristic algorithms
Iterative methods
linear fuzzy information granule (LFIG)
l₁ trend filtering
Merging
Prototypes
Segments
Time measurement
Time series
Time series analysis
time-series clustering
Trends
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:This article aims to design a trend-oriented-granulation-based fuzzy <inline-formula> <tex-math notation="LaTeX">C </tex-math></inline-formula>-means (FCM) algorithm that can cluster a group of time series at an abstract (granular) level. To achieve a better trend-oriented granulation of a time series, <inline-formula> <tex-math notation="LaTeX">{l}_{1} </tex-math></inline-formula> trend filtering is firstly carried out to result in segments which are then optimized by the proposed segment merging algorithm. By constructing a linear fuzzy information granule (LFIG) on each segment, a granular time series which well reflects the linear trend characteristic of the original time series is produced. With the novel designed distance that can well measure the trend similarity of two LFIGs, the distance between two granular time series is calculated by the modified dynamic time warping (DTW) algorithm. Based on this distance, the LFIG-based FCM algorithm is developed for clustering time series. In this algorithm, cluster prototypes are iteratively updated by the specifically designed granule splitting and merging algorithm, which allows the lengths of prototypes to change in the process of iteration. This overcomes the serious drawback of the existing approaches, where the lengths of prototypes cannot be changed. Experimental studies demonstrate the superior performance of the proposed algorithm in clustering time series with different shapes or trends.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3184999