Clustering Analysis Based On Swarm Intelligence

Data clustering is an effective method of analyzing data and finding out useful information from data. Clustering analysis is the organization of a collection of patterns into clusters based on similarity. By clustering analysis, people can discover the global distributed model and interesting correlation of data attributes. In recent years clustering analysis has been an important method in data mining domain.swarm intelligence is a novel simulated evolutionary computation technology. Its inspiration came from of some mechanism in biological system. It has special relationships with artificial life, evolution strategies and genetic algorithm. Much theory of research and application related to swarm intelligence proves that it is a kind of effective method to solve many global optimal problems.In view of the features of swarm intelligence, this paper focuses on the improvement of clustering algorithms based on ACO and PSO. The main research content of this paper includes:1 A multi-agent ant clustering algorithm is proposed. A novel multi-agent idea is introduced in the algorithm. The algorithm improved on the computation method of fitness and the value of some parameters. In the algorithm, each ant represents a data object. It will decide its next moving position according to similarity function and probability converting function between it and its neighbors. At the same time it will update its cluster number according to clustering rules. Each ant only depends on a little local information to find proper cluster. The experiment results demonstrated effectiveness of the algorithm.2 An improved method named PSO clustering algorithm based on cooperative evolution is proposed. Cooperative evolutionary strategy with multi-populations is introduced in the algorithm. The global optimum will be passed in all sub-populations. The whole clustering process is divided into two stages. The first stage uses the cooperative evolutionary PSO algorithm to search the initial clustering centers. The second stage uses the K-means algorithm. The algorithm can decide the number of cluster automatically. The clustering result is not influence by the iniatial clustering centers.3 A novel point symmetry-based PSO clustering algorithm is proposed. It could defect the number of clusters and the proper partitions from data sets when they possess the property of symmetry. In the proposed algorithm, assignment of points is done based on point symmetry based distance rather than the Euclid distance. A newly point symmetry based distance cluster fitness is used as a measure of the validity of the corresponding partitioning. The algorithm could make the clustering results more practical. The effectiveness of the algorithm compared to standard clustering algorithm is demonstrated for different data sets.Innovative viewpoints of this dissertation are summarized as follows:1 An improved multi-agent ant clustering algorithm is proposed. In the algorithm, multi-agents idea is introduced. The algorithm improved the computation method of fitness and the setting method of parameters. By the use of the algorithm, the clustering speed increased greatly.2 PSO clustering algorithm based on cooperative evolution is proposed. Cooperative evolution idea is introduced in the algorithm. The algorithm makes the best of the stochastic search strategy of PSO and the local optimized mechanism of K-means. The clustering result is not influenced by the initial cluster centers.3 Point symmetry based distance PSO clustering algorithm is proposed. It can decide the number of clusters and detect the proper partitions when they possess the property of symmetry. Assignment of points to different clusters is done based on point symmetry based distance. It makes the clustering results more practical.