Characterization and detection of noise in clustering
Pattern Recognition Letters
Fast ISODATA clustering algorithms
Pattern Recognition
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Principles of data mining
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Collaborative fuzzy clustering
Pattern Recognition Letters
ReCoM: reinforcement clustering of multi-type interrelated data objects
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Metastructural facets of granular computing
International Journal of Knowledge Engineering and Soft Data Paradigms
Collaborative architectures of fuzzy modeling
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Learning in parallel universes
Data Mining and Knowledge Discovery
Supervised learning in parallel universes using neighborgrams
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
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We present an extension of the fuzzy c-Means algorithm, which operates simultaneously on different feature spaces-so-called parallel universes-and also incorporates noise detection. The method assigns membership values of patterns to different universes, which are then adopted throughout the training. This leads to better clustering results since patterns not contributing to clustering in a universe are (completely or partially) ignored. The method also uses an auxiliary universe to capture patterns that do not contribute to any of the clusters in the real universes and therefore are likely to represent noise. The outcome of the algorithm is clusters distributed over different parallel universes, each modeling a particular, potentially overlapping subset of the data and a set of patterns detected as noise. One potential target application of the proposed method is biological data analysis where different descriptors for molecules are available but none of them by itself shows global satisfactory prediction results.