A genetic clustering algorithm using a message-based similarity measure

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.

► A new similarity measure based on message passing is proposed.
► The message passing between data points and the candidate centers.
► A set of problem-specific evolutionary operators are given.
► Our algorithm is able to evolve and find the optimal number of clusters and centers.
► A new cost function which penalizes the clusters that have more clusters is defined.