Entropy-based fuzzy clustering and fuzzy modeling
Fuzzy Sets and Systems
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
Statistical Mechanical Analysis of Fuzzy Clustering Based on Fuzzy Entropy
IEICE - Transactions on Information and Systems
Time series analysis with multiple resolutions
Information Systems
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Robust kernel FCM in segmentation of breast medical images
Expert Systems with Applications: An International Journal
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
Complexity reduction for "large image" processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel intuitionistic fuzzy clustering method for geo-demographic analysis
Expert Systems with Applications: An International Journal
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
Survey of Clustering: Algorithms and Applications
International Journal of Information Retrieval Research
Hi-index | 12.06 |
Clustering is a well known technique in identifying intrinsic structures and find out useful information from large amount of data. One of the most extensively used clustering techniques is the fuzzy c-means algorithm. However, computational task becomes a problem in standard objective function of fuzzy c-means due to large amount of data, measurement uncertainty in data objects. Further, the fuzzy c-means suffer to set the optimal parameters for the clustering method. Hence the goal of this paper is to produce an alternative generalization of FCM clustering techniques in order to deal with the more complicated data; called quadratic entropy based fuzzy c-means. This paper is dealing with the effective quadratic entropy fuzzy c-means using the combination of regularization function, quadratic terms, mean distance functions, and kernel distance functions. It gives a complete framework of quadratic entropy approaching for constructing effective quadratic entropy based fuzzy clustering algorithms. This paper establishes an effective way of estimating memberships and updating centers by minimizing the proposed objective functions. In order to reduce the number iterations of proposed techniques this article proposes a new algorithm to initialize the cluster centers. In order to obtain the cluster validity and choosing the number of clusters in using proposed techniques, we use silhouette method. First time, this paper segments the synthetic control chart time series directly using our proposed methods for examining the performance of methods and it shows that the proposed clustering techniques have advantages over the existing standard FCM and very recent ClusterM-k-NN in segmenting synthetic control chart time series.