Efficient Implementation of the Fuzzy c-Means Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Comparison of non-stationary time series in the frequency domain
Computational Statistics & Data Analysis
Crisp and fuzzy k-means clustering algorithms for multivariate functional data
Computational Statistics
A clustering procedure for exploratory mining of vector time series
Pattern Recognition
Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques
Journal of Classification
On fuzzy cluster validity indices
Fuzzy Sets and Systems
The wavelet-based cluster analysis for temporal gene expression data
EURASIP Journal on Bioinformatics and Systems Biology
Time series clustering based on forecast densities
Computational Statistics & Data Analysis
A weighted fuzzy c-means clustering model for fuzzy data
Computational Statistics & Data Analysis
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Clustering of unevenly sampled gene expression time-series data
Fuzzy Sets and Systems
Clustering of time series data-a survey
Pattern Recognition
Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Wavelets-based clustering of multivariate time series
Fuzzy Sets and Systems
A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples
Computational Statistics & Data Analysis
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The traditional approaches to clustering a set of time series are generally applicable if there is a fixed underlying structure to the time series so that each will belong to one cluster or the other. However, time series often display dynamic behaviour in their evolution over time. This dynamic behaviour should be taken into account when attempting to cluster time series. For instance, during a certain period, a time series might belong to a certain cluster; afterwards its dynamics might be closer to that of another cluster. In this case, the traditional clustering approaches are unlikely to find and represent the underlying structure in the given time series. This switch from one time state to another, which is typically vague, can be naturally treated following a fuzzy approach. This paper proposes a fuzzy clustering approach based on the autocorrelation functions of time series, in which each time series is not assigned exclusively to only one cluster, but it is allowed to belong to different clusters with various membership degrees.