Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Fuzzy Sets and Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Microarray Time-Series Data Clustering Using Rough-Fuzzy C-Means Algorithm
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
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Data clustering algorithms are used in many fields like anonymisation of databases, image processing, analysis of satellite images and medical data analysis. There are several C-Means clustering algorithms in the literature. Besides the hard C-Means, there are uncertainty based C-Means algorithms like the Fuzzy C-Means algorithm and its variants, the Rough C-Means algorithm, the Intuitionistic Fuzzy C- Means algorithm and the hybrid C-Means algorithms (Rough Fuzzy C-Means algorithm). In this paper we propose a new hybrid clustering algorithm called Rough Intuitionistic Fuzzy C-Means and evaluate its performance in comparison to the other algorithms mentioned above. We have applied these algorithms on numerical as well as image data of two different types and used some benchmarking indexes for the evaluation of their performance. The results show that the proposed algorithm outperforms the existing algorithms in almost all cases.