Fuzzy measures in determining seed points in clustering
Pattern Recognition Letters
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Neural expert system using fuzzy teaching input and its application to medical diagnosis
Information Sciences—Applications: An International Journal
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Applied Intelligence
Web Intelligence and Agent Systems
Generalized fuzzy rough sets determined by a triangular norm
Information Sciences: an International Journal
The many facets of natural computing
Communications of the ACM
A granular computing framework for self-organizing maps
Neurocomputing
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
Generalized rough sets, entropy, and image ambiguity measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Fuzzy rough granular neural networks, fuzzy granules, and classification
Theoretical Computer Science
Fuzzy rough granular self organizing map
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Range-Based Distance Measure for Microarray Expressions and a Fast Gene-Ordering Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging
Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging
RNA Secondary Structure Prediction Using Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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A fuzzy rough granular self-organizing map (FRGSOM) involving a 3-dimensional linguistic vector and connection weights, defined in an unsupervised manner, is proposed for clustering patterns having overlapping regions. Each feature of a pattern is transformed into a 3-dimensional granular space using a @p-membership function with centers and scaling factors corresponding to the linguistic terms low, medium or high. The three-dimensional linguistic vectors are then used to develop granulation structures, based on a user defined @a-value. The granulation structures are labeled with integer values representing the crisp decision classes. These structures are presented in a decision table, which is used to determine the dependency factors of the conditional attributes using the concept of fuzzy rough sets. The dependency factors are used as initial connection weights of the proposed FRGSOM. The FRGSOM is then trained through a competitive learning of the self-organizing map. We also propose a new ''fuzzy rough entropy measure'', based on the resulting clusters and using the concept of fuzzy rough sets. The effectiveness of the FRGSOM and the utility of ''fuzzy rough entropy'' in evaluating cluster quality are demonstrated on different real life datasets, including microarrays, with varying dimensions.