ACM Computing Surveys (CSUR)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
A genetic fuzzy k-Modes algorithm for clustering categorical data
Expert Systems with Applications: An International Journal
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Integrating clustering and supervised learning for categorical data analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new multi-objective technique for differential fuzzy clustering
Applied Soft Computing
A dissimilarity measure for the k-Modes clustering algorithm
Knowledge-Based Systems
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough Set Based Generalized Fuzzy -Means Algorithm and Quantitative Indices
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy k-modes algorithm for clustering categorical data
IEEE Transactions on Fuzzy Systems
Attribute value weighting in k-modes clustering
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
With the growing demand of categorical data clustering, a new hybrid clustering algorithm, namely Rough set based Fuzzy K-Modes, is proposed in this paper. The principles of rough and fuzzy sets are used in integrated form. It gives the better handling of uncertainty, vagueness, and incompleteness in class definition, while using the concept of lower and upper approximations of rough, on the other hand, the membership function of fuzzy sets enables efficient handling of overlapping partitions. Superiority of the proposed method over state-of-the-art methods is demonstrated quantitatively. For this purpose, two artificial and two real life categorical data sets are used. Also statistical significance test has been carried out to establish the statistical significance of the proposed clustering results.