A genetic clustering algorithm using a message-based similarity measure
Expert Systems with Applications(2012)
Abstract
In this paper, a genetic clustering algorithm is described that uses a new similarity measure based message passing between data points and the candidate centers described by the chromosome. In the new algorithm, a variable-length real-value chromosome representation and a set of problem-specific evolutionary operators are used. Therefore, the proposed GA with message-based similarity (GAMS) clustering algorithm is able to automatically evolve and find the optimal number of clusters as well as proper clusters of the data set. Effectiveness of GAMS clustering algorithm is demonstrated for both artificial and real-life data set. Experiment results demonstrated that the GAMS clustering algorithm has high performance, effectiveness and flexibility.
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Key words
real-life data,variable-length real-value chromosome representation,message-based similarity,data point,genetic clustering algorithm,gams clustering algorithm,new similarity measure,new algorithm,clustering algorithm,message-based similarity measure,message passing,clustering,evolutionary computation,k means algorithm,genetic algorithms
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