L1-norm based fuzzy clustering
Fuzzy Sets and Systems
Nonparametric classifier design using greedy tree-structured vector quantization technique
Pattern Recognition Letters
Entropy-based fuzzy clustering and fuzzy modeling
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Fuzzy c-Means Classifier for Incomplete Data Sets with Outliers and Missing Values
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Standardized course generation process using Dynamic Fuzzy Petri Nets
Expert Systems with Applications: An International Journal
Data spread-based entropy clustering method using adaptive learning
Expert Systems with Applications: An International Journal
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Numerical methods for fuzzy clustering
Information Sciences: an International Journal
Extended Gaussian kernel version of fuzzy c-means in the problem of data analyzing
Expert Systems with Applications: An International Journal
Adapted Mean Variable Distance to Fuzzy-Cmeans for Effective Image Clustering
RVSP '11 Proceedings of the 2011 First International Conference on Robot, Vision and Signal Processing
Hybrid algorithms with instance-based classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
Possibilistic approach to kernel-based fuzzy c-means clustering with entropy regularization
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Complexity reduction for "large image" processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In recent years, various data analysis techniques have been developed for extracting meaningful information from real-world data clustering problems. The results, running time, and clustering validity of the techniques are very important. During few decades, fuzzy clustering algorithms and especially the fuzzy c-means FCM algorithm has been widely utilized for solving data clustering problems. The fuzzy c-means algorithm FCM can perform well when applied to noise-free datasets, but performs somewhat poorly when applied to data that have been corrupted with noise, mainly because of the use of the non-robust objective function of FCM and the typical Euclidean distance measure of similarity or dissimilarity. To overcome these shortcomings, this work establishes effective objective functions of fuzzy c-means with the center learning method-based quadratic mean distance, entropy methods, and regularization terms. The effective membership function is derived and center updating by optimizing the proposed effective methods. This work introduces a center learning method to reduce the computational complexity and running time. Also, the proposed methods are applied to artificial data, checkerboard, and real-world datasets to evaluate their performance. The silhouette method is used to find the clustering accuracy of the proposed methods with those of other clustering methods. The experimental results reveal the advantages of the proposed clustering for application to real datasets and random data. They also reveal that the proposed methods outperform the other methods.