DocumentCode :
3570918
Title :
Finding the most evident co-clusters on web log dataset using frequent super-sequence mining
Author :
Xinran Yu ; Korkmaz, Turgay
Author_Institution :
Comput. Sci. Dept., Univ. of Texas at San Antonio, San Antonio, TX, USA
fYear :
2014
Firstpage :
529
Lastpage :
536
Abstract :
It is important to mine the weblog dataset to find interesting and helpful information. There are three kinds of mining on weblog data which are web usage mining, web structure mining and web content mining. In our research, we are going to investigate web pages structure and find the most evident groups of users and web pages. Nowadays, big data is everywhere. Facing huge amount of web logs, it is not always necessary to group all the users in a web log dataset into different clusters, sometimes, finding out the major dominant user groups and the corresponding web pages is more important. In this paper, we are going to investigate a new way to search the most evident co-clusters of users and the corresponding web pages in the web log dataset using frequent super-sequence mining technique. Through experiments we find interesting results.
Keywords :
Web sites; data mining; pattern clustering; Web content mining; Web log dataset mining; Web page structure; Web structure mining; Web usage mining; frequent super-sequence mining technique; most evident user coclusters; Clustering algorithms; Data mining; Databases; Market research; Merging; Phase change materials; Web pages;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on
Type :
conf
DOI :
10.1109/IRI.2014.7051935
Filename :
7051935
Link To Document :
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