Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A novel initialization scheme for the fuzzy c-means algorithm for color clustering
Pattern Recognition Letters
Kernelized fuzzy attribute C-means clustering algorithm
Fuzzy Sets and Systems
A cluster validity index for fuzzy clustering
Fuzzy Sets and Systems
Fuzzy C-means based clustering for linearly and nonlinearly separable data
Pattern Recognition
From visual data exploration to visual data mining: a survey
IEEE Transactions on Visualization and Computer Graphics
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
As it is known, fuzzy clustering is a kind of soft clustering method and primarily based on idea of segmenting data by using membership degrees of cases which are computed for each cluster. However, most of the current fuzzy clustering modules packaged in both open source and commercial products have lack of enabling users to explore fuzzy clusters deeply and visually in terms of investigation of different relations among clusters. Furthermore, without a decision maker or an expert, it is hard to decide the number of clusters in fuzzy clustering studies. Therefore, in this study, a desktop software, namely FUAT, is developed to analyze, explore and visualize different aspects of obtained fuzzy clusters which are segmented by fuzzy c-means algorithm. Moreover, to obtain and inform possible natural cluster number, FUAT is equipped with Expectation Maximization algorithm.