A fuzzy multistage evolutionary (FUME) clustering technique

Authors:
B Bharathi Devi;V. V. S Sarma
Affiliations:
School of Automation, Indian Institute of Science, Bangalore 560012, India;School of Automation, Indian Institute of Science, Bangalore 560012, India
Venue:
Pattern Recognition Letters
Year:
1984

Citing 5
Cited 1

Fuzzy Sets and Systems: Theory and Applications

Fuzzy Sets and Systems: Theory and Applications
Fuzzy Switching and Automata: Theory and Applications

Fuzzy Switching and Automata: Theory and Applications
Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs

IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster Validity for the Fuzzy c-Means Clustering Algorithrm

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Effect of Cluster Size, Dimensionality, and the Number of Clusters on Recovery of True Cluster Structure

IEEE Transactions on Pattern Analysis and Machine Intelligence

An algorithm to compute the supremum of max-min powers and a property of fuzzy graphs

Pattern Recognition Letters

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this paper, a multistage evolutionary scheme is proposed for clustering in a large data base, like speech data. This is achieved by clustering a small subset of the entire sample set in each stage and treating the cluster centroids so obtained as samples, together with another subset of samples not considered previously, as input data to the next stage. This is continued till the whole sample set is exhausted. The clustering is accomplished by constructing a fuzzy similarity matrix and using the fuzzy techniques proposed here. The technique is illustrated by an efficient scheme for voiced-unvoiced-silence classification of speech.