首页    期刊浏览 2024年10月07日 星期一
登录注册

文章基本信息

  • 标题:A Novel Document Clustering Algorithm Using Squared Distance Optimization Through Genetic Algorithms
  • 本地全文:下载
  • 作者:Harish Verma ; Eatesh Kandpal ; Bipul Pandey
  • 期刊名称:International Journal on Computer Science and Engineering
  • 印刷版ISSN:2229-5631
  • 电子版ISSN:0975-3397
  • 出版年度:2010
  • 卷号:2
  • 期号:5
  • 页码:1875-1879
  • 出版社:Engg Journals Publications
  • 摘要:K-Means Algorithm is most widely used algorithms in document clustering. However, it still suffer some shortcomings like random initialization, solution converges to local minima, and empty cluster formation. Genetic algorithm is often used for document clustering because of its global search and optimization ability over heuristic problems. In this paper, search ability of genetic algorithm has exploited with a modification from the general genetic algorithm by not using the random initial population.A new algorithm for population initialization is given in this paper and results are compared with k-means algorithm.
  • 关键词:Genetic algorithm; optimization; Document clustering; k-means; mutation; crossover.
国家哲学社会科学文献中心版权所有