Web Usage Mining - MapReduce-based Emerging Pattern Mining in Hypergraph Learning

Xiuming Yu, Meijing Li, Keun Ho Ryu

2015

Abstract

Web usage mining is a popular research area in data mining. As the rapid growth of internet, more and more log information is collected by the web servers around the world. It becomes difficult to extract useful information from these huge web log data. Classic techniques of web usage mining are performed with low efficency in large number of web log data, because a lot of system resource is needed to deal with large computation. Most techniques of web mining are performed based on assosiation rule mining or frequent pattern mining, which aim to find relationships among web pages or predicting the behavoir of web users. That’s difficult to find some favourite web pages in different web users. In this research, we propose an efficient approach to find some favourite web pages in different web users in large web log data based on hypergraph learning by considering the programming model of MapReduce and the techniques of emerging pattern mining.

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Paper Citation


in Harvard Style

Yu X., Li M. and Ryu K. (2015). Web Usage Mining - MapReduce-based Emerging Pattern Mining in Hypergraph Learning . In Doctoral Consortium - DCPRAM, (ICPRAM 2015) ISBN , pages 14-18


in Bibtex Style

@conference{dcpram15,
author={Xiuming Yu and Meijing Li and Keun Ho Ryu},
title={Web Usage Mining - MapReduce-based Emerging Pattern Mining in Hypergraph Learning},
booktitle={Doctoral Consortium - DCPRAM, (ICPRAM 2015)},
year={2015},
pages={14-18},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCPRAM, (ICPRAM 2015)
TI - Web Usage Mining - MapReduce-based Emerging Pattern Mining in Hypergraph Learning
SN -
AU - Yu X.
AU - Li M.
AU - Ryu K.
PY - 2015
SP - 14
EP - 18
DO -