Hybrid Particle Swarm Optimization and Genetic Algorithm for Dynamic Clustering Applied to Documents

• Main Authors: Wan -Ju Lin, 林宛儒
• Other Authors: Yen-Ju Yang
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/16066534073165054196

碩士 === 大同大學 === 資訊經營學系(所) === 100 === Because of the rapid development of the Internet, resulting in the widespread of information (Information overflow), we need more effective way to manage the entire information managementfrom raw data and further becoming knowledge,. Ccluster analysis grouping similar data into togaethering, to is one of the solutions solve this problem. The new clustering techniquesology hasve constantly been proposed, but most of them must the clustering methods are necessary to define in advance the number of groupsing in advance, which is very difficult for practical applications. is difficult to decide in advance the appropriate number of clusters. Therefore, this study research proposed a hybrid algorithm based on the particle swarm optimization dynamic clustering algorithm (Dynamic Clustering using Particle Swarm Optimization, (DCPSO) [9] to get the particles with best cluster numbers, and then integration of based on the number of genes with unknown group clustering algorithm (Genetic Clustering for Unknown K, (GCUK) [10] by its floating-point operations of the replication, crossover, and mutation to perform the evolution of the position of cluster centersin the particle position changes, in addition, survival of the fitter particles and elimination of the worse. and keep the good particles out of the poor particles, thereby avoiding the limitations of the optimal solution in the region, while the optimal solution to be global. This can be made by the Institute of hybrid According to the experimental results, the proposed dynamic clustering algorithm, (Hybrid Particle Swarm Optimization and Genetic Algorithm for Dynamic Clustering, (HPSOGADC), can automatically determine the appropriate grouping cluster number, and get a goodbetter clustering quality. According to the experiment shown in this study and the proposed hybrid algorithm than GCUK alone or DCPSO alone. or compare differences in data centers with smaller groups that better clustering quality while the number of clusters determined closer to the correct answer. According to this research model, used in high-dimensional clustering properties of the file set of problems can be more efficient to file a Applying the proposed good algorithm to high-dimensional document clustering problem, the text with similar theme will be effectively gathered together than other document clustering methods. were gathered in groups, It is really can to help users quickly retrieve the necessary and useful information.