A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Estimating the number of segments in time series data using permutation tests
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modified Gath--Geva clustering for fuzzy segmentation of multivariate time-series
Fuzzy Sets and Systems
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Monitoring of complex systems of interacting dynamic systems
Applied Intelligence
Statistical user model supported by R-Tree structure
Applied Intelligence
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In this paper, a new weighted and constrained possibilistic C-means clustering algorithm is proposed for process fault detection and diagnosis (FDI) in offline and online modes for both already known and novel faults. A possibilistic clustering based approach is utilized here to address some of the deficiencies of the fuzzy C-means (FCM) algorithm leading to more consistent results in the context of the FDI tasks by relaxing the probabilistic condition in FCM cost function. The proposed algorithm clusters the historical data set into C different dense regions without having precise knowledge about the number of the faults in the data set. The algorithm incorporates simultaneously possibilistic algorithm and local attribute weighting for time-series segmentation. This allows different weights to be allocated to different features responsible for the distinguished process faults which is an essential characteristic of proper FDI operations. A set of comparative studies have been carried out on the large-scale Tennessee Eastman industrial challenge problem and the DAMADICS actuator benchmark to demonstrate the superiority of the proposed algorithm in process FDI applications with respect to some available alternative approaches.