How many clusters are best?—an experiment
Pattern Recognition
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Clustering Based on Modified Distance Measures
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
IEEE Transactions on Knowledge and Data Engineering
An new initialization method for fuzzy c-means algorithm
Fuzzy Optimization and Decision Making
Studies on Fuzzy C-Means Based on Ant Colony Algorithm
ICMTMA '10 Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 03
Integrating clustering and supervised learning for categorical data analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Expert Systems with Applications: An International Journal
Relaxed constraints support vector machines for noisy data
Neural Computing and Applications - Special Issue on WCCI2008
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
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Fuzzy clustering is a widely applied method for extracting the underlying models within data. It has been applied successfully in many real-world applications. Fuzzy c-means is one of the most popular fuzzy clustering methods because it produces reasonable results and its implementation is straightforward. One problem with all fuzzy clustering algorithms such as fuzzy c-means is that some data points which are assigned to some clusters have low membership values. It is possible that many samples may be assigned to a cluster with low-confidence. In this paper, an efficient and noise-aware implementation of support vector machines, namely relaxed constraints support vector machines, is used to solve the mentioned problem and improve the performance of fuzzy c-means algorithm. First, fuzzy c-means partitions data into appropriate clusters. Then, the samples with high membership values in each cluster are selected for training a multi-class relaxed constraints support vector machine classifier. Finally, the class labels of the remaining data points are predicted by the latter classifier. The performance of the proposed clustering method is evaluated by quantitative measures such as cluster entropy and Minkowski scores. Experimental results on real-life data sets show the superiority of the proposed method.