Mixtures of probabilistic principal component analyzers
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining and Knowledge Discovery for Process Monitoring and Control
Fuzzy Model Identification for Control
Fuzzy Model Identification for Control
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Estimating the number of segments in time series data using permutation tests
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
IEEE Transactions on Computers
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Learning recurrent behaviors from heterogeneous multivariate time-series
Artificial Intelligence in Medicine
A segmentation-based approach for temporal analysis of software version repositories
Journal of Software Maintenance and Evolution: Research and Practice
A review on time series data mining
Engineering Applications of Artificial Intelligence
Pattern recognition and classification for multivariate time series
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Pattern recognition in multivariate time series: dissertation proposal
Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Wavelets-based clustering of multivariate time series
Fuzzy Sets and Systems
Multivariate time series segmentation for generalized description of dynamic systems operation
Optical Memory and Neural Networks
Correlation based dynamic time warping of multivariate time series
Expert Systems with Applications: An International Journal
Determination of temporal information granules to improve forecasting in fuzzy time series
Expert Systems with Applications: An International Journal
The modeling of time series based on fuzzy information granules
Expert Systems with Applications: An International Journal
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Partitioning a time-series into internally homogeneous segments is an important data-mining problem. The changes of the variables of a multivariate time-series are usually vague and do not focus on any particular time point. Therefore, it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to group overlapping and vague objects, they cannot be directly applied to time-series segmentation, because the clusters need to be contiguous in time. This paper proposes a clustering algorithm for the simultaneous identification of local probabilistic principal component analysis (PPCA) models used to measure the homogeneity of the segments and fuzzy sets used to represent the segments in time. The algorithm favors contiguous clusters in time and is able to detect changes in the hidden structure of multivariate time-series. A fuzzy decision making algorithm based on a compatibility criteria of the clusters has been worked out to determine the required number of segments, while the required number of principal components are determined by the screeplots of the eigenvalues of the fuzzy covariance matrices. The application example shows that this new technique is a useful tool for the analysis of historical process data.