Single-Objective/Multiobjective Cat Swarm Optimization Clustering Analysis for Data Partition.

This article proposes single-objective/multiobjective cat swarm optimization clustering algorithms for data partition. The proposed methods use the cat swarm to search the optimal. The position of the cat tightly associates with the clustering centers and is updated by two submodes: the seeking mode and the tracing mode. The seeking mode uses the simulated annealing strategy to update the cat position at a probability. Inspired by the quantum theories, the tracing mode adopts the quantum model to update the cat position in the whole solution space. First, the single-objective method is proposed and adopts the cohesion of clustering as the objective function, in which the kernel method is applied. For considering more objective functions to reveal diverse aspects of data, the multiobjective method is proposed and adopts both the cohesion and the connectivity as the objective functions. The Pareto optimization method is applied to balance the objectives. In the experiments, three kinds of data sets are used to examine the effectiveness of the proposed methods, which are three synthetic data sets, four data sets from the UCI Machine Learning Repository, and a field data set. Experimental results verified that the proposed methods perform better than the traditional clustering algorithms, and the proposed multiobjective method has the highest accuracy. Note to Practitioners—This article presents single-objective/multiobjective cat swarm optimization clustering analysis methods for data partition. Through automatically extracting meaningful or useful classes, clustering analysis could help the practitioners or the intelligent devices find the specific meanings of data, natural data structure, the data relationships, or other characteristics. The proposed methods use the cat swarm to search the optimal clustering result. One or more criterion functions could be selected as the optimization objectives. The time complexity of the multiobjective type is higher than that of the single-objective type. Therefore, in the industrial field, engineers should choose the number of the optimization objectives based on the actual requirements. The proposed methods could be widely used into industrial applications to deal with complex data sets. Future research could consider some more progressive optimization schemes to improve the effectiveness. [ABSTRACT FROM AUTHOR]