Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
ACM Computing Surveys (CSUR)
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
A new cluster validity measure and its application to image compression
Pattern Analysis & Applications
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Clustering
Metaheuristic Clustering
Fuzzy system parameters discovery by bacterial evolutionary algorithm
IEEE Transactions on Fuzzy Systems
Stability analysis of the reproduction operator in bacterial foraging optimization
Theoretical Computer Science
Gene transposon based clone selection algorithm for automatic clustering
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
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This paper describes an evolutionary clustering algorithm, which can partition a given dataset automatically into the optimal number of groups through one shot of optimization. The proposed method is based on an evolutionary computing technique known as the Bacterial Evolutionary Algorithm (BEA). The BEA draws inspiration from a biological phenomenon of microbial evolution. Unlike the conventional mutation, crossover and selection operaions in a GA (Genetic Algorithm), BEA incorporates two special operations for evolving its population, namely the bacterial mutation and the gene transfer operation. In the present context, these operations have been modified so as to handle the variable lengths of the chromosomes that encode different cluster groupings. Experiments were done with several synthetic as well as real life data sets including a remote sensing satellite image data. The results estabish the superiority of the proposed approach in terms of final accuracy.