Journal ArticleDOI
Unsupervised optimal fuzzy clustering
Isak Gath,Amir B. Geva +1 more
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TLDR
The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster.Abstract:
This study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolume and density criteria. An algorithm is derived from a combination of the fuzzy K-means algorithm and fuzzy maximum-likelihood estimation. The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. The algorithm was tested on different classes of simulated data, and on a real data set derived from sleep EEG signal. >read more
Citations
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Journal ArticleDOI
Survey of clustering algorithms
Rui Xu,Donald C. Wunsch +1 more
TL;DR: Clustering algorithms for data sets appearing in statistics, computer science, and machine learning are surveyed, and their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts are illustrated.
Journal ArticleDOI
A validity measure for fuzzy clustering
X.L. Xie,Gerardo Beni +1 more
TL;DR: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.
Journal ArticleDOI
On Clustering Validation Techniques
TL;DR: The fundamental concepts of clustering are introduced while it surveys the widely known clustering algorithms in a comparative way and the issues that are under-addressed by the recent algorithms are illustrated.
Journal ArticleDOI
A possibilistic approach to clustering
TL;DR: An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Journal ArticleDOI
Clustering of time series data-a survey
TL;DR: This paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains, including general-purpose clustering algorithms commonly used in time series clustering studies.
References
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Book
Pattern Recognition with Fuzzy Objective Function Algorithms
TL;DR: Books, as a source that may involve the facts, opinion, literature, religion, and many others are the great friends to join with, becomes what you need to get.
Journal ArticleDOI
A Cluster Separation Measure
TL;DR: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster which can be used to infer the appropriateness of data partitions.
Proceedings ArticleDOI
Fuzzy clustering with a fuzzy covariance matrix
TL;DR: Experimental results are presented which indicate that more accurate clustering may be obtained by using fuzzy covariances, a natural approach to fuzzy clustering.
Journal ArticleDOI
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
TL;DR: A family of graph-theoretical algorithms based on the minimal spanning tree are capable of detecting several kinds of cluster structure in arbitrary point sets; description of the detected clusters is possible in some cases by extensions of the method.