Abstract
Since web pages visited by users contain a variety of data resources and the clustering algorithms frequently used for web data do not take the heterogeneous nature into account when processing the heterogeneous data, this paper proposes a new algorithm, namely IHPSOC algorithm, to cluster web log data on the basis of web log mining. Based on particle swarm optimization (PSO), IHPSOC algorithm clusters the web log data through particle swarm iteration. Based on clustering results, this paper establishes Markov chain-like models which create a corresponding Markov chain for users in each different category so as to predict the web resources in users' need. The results of the experiments show that the proposed model gives better predication.
Original language | English |
---|---|
Pages (from-to) | 653-674 |
Number of pages | 22 |
Journal | International Journal of Software Engineering and Knowledge Engineering |
Volume | 26 |
Issue number | 4 |
DOIs | |
Publication status | Published - May 2016 |
Keywords
- Data clustering
- Markov chain-like model
- particle swarm optimization
- web log mining
ASJC Scopus subject areas
- Software
- Computer Networks and Communications
- Computer Graphics and Computer-Aided Design
- Artificial Intelligence