Rough clustering for web transactions

• Main Author: Yanto, Iwan Tri Riyadi
• Format: Thesis
• Language: English; English; English
• Published: 2011
• Subjects: QA Mathematics; QA76 Computer software
• Online Access: http://eprints.uthm.edu.my/2629/

Grouping web transactions into clusters is important in order to obtain better understanding of user's behavior. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of transactions by given threshold. However, the processing time is still an issue due to the high complexity for finding the similarity of upper approximations of a transaction which used to merge between two or more clusters. In this study, an alternative technique for grouping web transactions using rough set theory is proposed. It is based on the two similarity classes which is nonvoid intersection. The technique is implemented in MATLAB ® version 7.6.0.324 (R2008a). The two UCI benchmark datasets taken from: http:/kdd.ics.uci.edu/ databases/msnbc/msnbc.html and http:/kdd.ics.uci.edu/databases/ Microsoft / microsoft.html are opted in the simulation processes. The simulation reveals that the proposed technique significantly requires lower response time up to 62.69 % and 66.82 % as compared to the rough approximation-based clustering, severally. Meanwhile, for cluster purity it performs better until 2.5 % and 14.47%, respectively.