A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification

  title={A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification},
  author={Malay Kumar Pakhira and Sanghamitra Bandyopadhyay and Ujjwal Maulik},
  journal={Fuzzy Sets Syst.},
A classification approach based on variable precision rough sets and cluster validity index function
This method combines a particle swarm optimization algorithm, fuzzy C-means method, variable precision rough sets theory, and a new cluster validity index function to cluster the values of the individual attributes within the data set.
An Empirical Study on Fuzzy Image Clustering with Various Clustering Validity Indexes
A new clustering validity index, WLI, that considers the median effects of image clustering using the fuzzy c-means (FCM) algorithm and has better performance on FCM-based image segmentation.
Estimation of optimal cluster number for fuzzy clustering with combined fuzzy entropy index
The results show the CFE index has superior performance in the estimation of the best partition of clusters than the indices PC, PE, MPC, XB, FS, Kwon, FHV and PBMF, especially for high dimensional datasets.
A cluster validity index for fuzzy clustering
  • B. Rezaee
  • Computer Science
    Fuzzy Sets Syst.
  • 2010
A Clustering Validity Index Based on Pairing Frequency
A new clustering validity index for both fuzzy and hard clustering algorithms that uses pairwise pattern information from a certain number of interrelated clustering results, which focus more on logical reasoning than geometrical features is proposed.
A New Fuzzy Clustering Validity Index With a Median Factor for Centroid-Based Clustering
A new clustering validity index, which is termed the Wu-and-Li index (WLI), is proposed, which partially allows, to some extent, the existence of closely allocated centroids in the clustering results by considering not only the minimum but the median distances between a pair of Centroids as well; therefore possessing better stability.
A Selection Model for Optimal Fuzzy Clustering Algorithm and Number of Clusters Based on Competitive Comprehensive Fuzzy Evaluation
A center initialization approach based on a minimum spanning tree to keep FCM from local minima and a selection model that combines multiple pairs of a fuzzy clustering algorithm and cluster validity index to identify the number of clusters and simultaneously selects the optimal fuzzy clusters for a dataset is proposed.


On cluster validity for the fuzzy c-means model
Limitation analysis indicates, and numerical experiments confirm, that the Fukuyama-Sugeno index is sensitive to both high and low values of m and may be unreliable because of this, and calculations suggest that the best choice for m is probably in the interval [1.5, 2.5], whose mean and midpoint, m=2, have often been the preferred choice for many users of FCM.
Clustering with a genetically optimized approach
A series random initializations of fuzzy/hard c-means, where the partition associated with the lowest J/sub m/ value is chosen, can produce an equivalent solution to the genetic guided clustering approach given the same amount of processor time in some domains.
Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification
The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. Real-coded variable string length genetic fuzzy
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
Abstract Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space. In both cases, the extremizing
Some new indexes of cluster validity
This work reviews two clustering algorithms and three indexes of crisp cluster validity and shows that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters.
Scaling genetically guided fuzzy clustering
  • L. Hall, B. Ozyurt
  • Computer Science
    Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society
  • 1995
Describes improvements to previous work on the use of genetic algorithms and evolutionary strategies to generate fuzzy partitions of unlabeled data. It was found that genetically guided clustering
Cluster Validity with Fuzzy Sets
This paper uses membership function matrices associated with fuzzy c-partitions of X, together with their values in the Euclidean (matrix) norm, to formulate an a posteriori method for evaluating algorithmically suggested clusterings of X.
A dendrite method for cluster analysis
A method for identifying clusters of points in a multidimensional Euclidean space is described and its application to taxonomy considered and an informal indicator of the "best number" of clusters is suggested.