Algorithms for clustering data
Algorithms for clustering data
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous and discrete wavelet transforms
SIAM Review
Parallel algorithms for hierarchical clustering
Parallel Computing
A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
On-line new event detection, clustering, and tracking (information retrieval, internet)
On-line new event detection, clustering, and tracking (information retrieval, internet)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
A comprehensive study of visual event computing
Multimedia Tools and Applications
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Adaptive learning of specific patterns or events of interest has been an area of significant research for various applications in the last two decades. In developing diagnostic evaluation and safety monitoring applications of a propulsion system, it is critical to detect, characterize and model events of interest. It is a challenging task since the detection system should allow adaptive characterization of potential events of interest and correlate them to learn new models for future detection for online health monitoring and diagnostic evaluation. In this paper, a novel framework is established using a hierarchical adaptive clustering approach with fuzzy membership functions to characterize specific events of interest from the measured and processed features. Raw engine measurement data is first analyzed using the wavelet transform to provide features for localization of frequency information for use in the classification system. A method combining hierarchical and fuzzy k-means clustering is then applied to a set of selected measurements and computed features to determine the events of interest during engine operations. Experimental results have shown that the proposed approach is effective and computationally efficient to detect, characterize and model new events of interest from data collected through continuous operations.