文章基本信息

标题：A Novel Web-Page Clustering Method Using K-Means Improved with Cellular Learning Automata And Genetic Algorithm
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作者：Peyman Almasinejad ; Mohammad Javad Kargar
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2017
卷号：17
期号：6
页码：278-286
出版社：International Journal of Computer Science and Network Security
摘要：Improvement of accuracy and optimal function of search engines has always been an area of concern for designers and researchers. Although these search engines work relatively well in simple searches, because the most existing search algorithms are based on search keywords, it can be expected for search engines to face trouble and confusion in some states of advanced searching. A possible solution is implementing Web Resources Categorization before performing the search. This study examines the basic web page clustering algorithms with the help of a k-means algorithm and optimizes its performance by solving its problems. The main issue is in the initial selection of clusters which can have a significant impact on the final clustering. Therefore, this research study proposes a new method for optimizing the core algorithm using cellular learning automata algorithm based on the Genetic Algorithm.
关键词：k-means Algorithm; Evolutionary Computation Algorithm; Cellular Learning Automata; Web Page Clustering; Genetic Algorithm