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
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
On Clustering Validation Techniques
Journal of Intelligent Information Systems
A robust and efficient clustering algorithm based on cohesion self-merging
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fuzzy cluster validation index based on inter-cluster proximity
Pattern Recognition Letters
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Class visualization of high-dimensional data with applications
Computational Statistics & Data Analysis
The fuzzy approach to statistical analysis
Computational Statistics & Data Analysis
On biological validity indices for soft clustering algorithms for gene expression data
Computational Statistics & Data Analysis
Pattern recognition approach to identify natural clusters of acoustic emission signals
Pattern Recognition Letters
| Hi-index | 0.03 |
We propose a new separation index that measures the magnitude of gaps between any two clusters in a partition, by projecting the data in a pair of clusters into a one-dimensional space in which they have the maximum separation. The resulting projections can also be used to determine partial membership for points near the boundaries between two or more clusters. The matrix of separation indexes is helpful in deciding whether too many or too few clusters are specified in the clustering method.