Study of Data Ming Classification based on Genetic Algorithm

摘要：

In view of genetic superiority in data mining algorithms, this paper combines the genetic algorithm and K-means algorithm and presents a genetic algorithm based k-means clustering algorithm and the algorithm to improve genetic clustering algorithm clustering using variable length actual real number of cluster center, and design a new crossover and mutation operators and the introduction of is widely used cluster validity index DB-Index as the target function, it not only better solve the K-means clustering algorithm, the number of clusters is difficult to determine the initial value of sensitivity and defects such as easy to fall into local optimum, and the algorithm efficiency and accuracy of the algorithm are greatly improved and compared with previous algorithms.

关键词： genetic algorithm data mining clustering

作者: XiaoFeng Li Chan Xin Li Li Yang

作者单位: Department of Computer AppliedTechnologyHuaDe School of AppliedTechnology of Harbin Institute ofTech Information Science and TechnologyCollegeHeilongjiang UniversityHarbin, China New Developing Educational Institution Harbin, China

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）