Research On Clustering Based On Immune Genetic Algorithm And Particle Swarm Optimization

With the development of information science and technology, people have inclined to collecting and organizing all kinds of data by computers, then the size of data has expended as well. When people have accumulated massive amount of business data, how to find the valuable information in the vast ocean-like data have become an urgent need to be solved. For this data mining techniques have emerged,which is one of the most cutting-edge research of the database and information decision-making. Cluster analysis as an important branch of data mining is the analysis of data's similarity, and divided the large data sets into groups, in which the data inside the same group was most similar to each other and the data in different groups was differ from each other. Clustering is an effective means of finding useful information. At present, Cluster analysis has been widely used in pattern recognition, data analysis, image processing, market research and many other fields.There is a large number of clustering algorithms in the literature. The choice of algorithm depends on the type of data, the purpose and applications of clustering. This paper discussed C-means clustering method which based on the immune genetic algorithm and particle swarm optimization algorithm separately. Following is the main work has been done:1. Complemented clustering algorithm with immune genetic algorithm. First, analyzed the strengths and weaknesses of the existing genetic algorithm, the immune mechanism was introduced into the standard genetic algorithm to overcome the premature phenomenon; Second, the C-means algorithm and the immune genetic algorithm were combined to form a hybrid algorithm; Finally, based on the actual situation of the clustering problem designed the genetic selection, crossover and mutation operators, made the hybrid algorithm converge to the global optimal solution much faster and more efficiently.2. Clustering with the improved particle swarm algorithm. First, the advantages and disadvantages of the existing particle swarm optimization were analyzed; second, the C-means algorithm which has strong local search ability and the genetic algorithm-based crossover and mutation operations were mixed into the particle swarm algorithm; finally, them have played their advantages respectively through appropriate regulation. Not only the PSO algorithm's ability of local search is improved, but also the diversity of the population was increased, at last achieved the purpose of prevent premature problem of the algorithm.3. Selected some data sets and the clustering experiments were implemented through MATLAB programming by the improved algorithms, and results were compared with other algorithms, and analyzed the result of the experiment.