Application of Particle Swarm Optimization and User Clustering in Web Search

被引：2
|
作者：
Ganesan, Sumathi [1 ]
Selvaraju, Sendhilkumar [1 ]
机构：
[1] Anna Univ, Dept Informat Sci & Technol, Madras 600025, Tamil Nadu, India
来源：
COMPUTATIONAL INTELLIGENCE IN DATA MINING, VOL 2 | 2015年 / 32卷
关键词：
Web search; User clustering; Swarm intelligence; Particle swarm optimization; ODP taxonomy;
D O I：
10.1007/978-81-322-2208-8_39
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
User clustering is the most significant process in web usage mining. This approach tries to generate the clusters of users with the similar travels in the web search. Preprocessing is needed to extract the relevant data which is used for user clustering. Now a day Particle Swarm Optimization (PSO) approach is used in web search applications. This paper applies a Particle Swarm Optimization algorithm to web user grouping in association with the Open Directory Project (ODP) dataset. The experimental result shows that the effectiveness of Particle Swarm Optimization to be a suitable approach for web user clustering as compared to the K-means and DB-Scan clustering methods.
引用
收藏
页码：427 / 437
页数：11
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