Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences—Intelligent Systems: An International Journal
A linear-time construction of the relative neighborhood graph from the Delaunay triangulation
Computational Geometry: Theory and Applications
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stability-based validation of clustering solutions
Neural Computation
A new cluster validity measure and its application to image compression
Pattern Analysis & Applications
New indices for cluster validity assessment
Pattern Recognition Letters
Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series)
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters
IEEE Transactions on Knowledge and Data Engineering
Finding groups in data: Cluster analysis with ants
Applied Soft Computing
Cluster Analysis
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Autonomous and deterministic supervised fuzzy clustering with data imputation capabilities
Applied Soft Computing
A new multi-objective technique for differential fuzzy clustering
Applied Soft Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A sober look at clustering stability
COLT'06 Proceedings of the 19th annual conference on Learning Theory
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy multi-layer perceptron, inferencing and rule generation
IEEE Transactions on Neural Networks
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Identification of the correct number of clusters and the appropriate partitioning technique are some important considerations in clustering where several cluster validity indices, primarily utilizing the Euclidean distance, have been used in the literature. In this paper a new measure of connectivity is incorporated in the definitions of seven cluster validity indices namely, DB-index, Dunn-index, Generalized Dunn-index, PS-index, I-index, XB-index and SV-index, thereby yielding seven new cluster validity indices which are able to automatically detect clusters of any shape, size or convexity as long as they are well-separated. Here connectivity is measured using a novel approach following the concept of relative neighborhood graph. It is empirically established that incorporation of the property of connectivity significantly improves the capabilities of these indices in identifying the appropriate number of clusters. The well-known clustering techniques, single linkage clustering technique and K-means clustering technique are used as the underlying partitioning algorithms. Results on eight artificially generated and three real-life data sets show that connectivity based Dunn-index performs the best as compared to all the other six indices. Comparisons are made with the original versions of these seven cluster validity indices.