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Mining Web Structures Using Multi-Dimensional Data Mining Model

Authors: Yoshinobu Yamashita; Tadashi Ohmori; Mamoru Hoshi;

Mining Web Structures Using Multi-Dimensional Data Mining Model

Abstract

Mining interesting structures of web organizations is a hot topic today. This paper proposes a method to mine non-trivial structures of an intranet-style web organiza,tion under multi-dimensional restrictions. To do so, we use our new datacube system named "ztemset cube". The itemset cube system does frequent itemset data-mining tasks in a datacube-style under multidimensional restrictions. Using this itemset cube, this paper analyzes structures of a web organization under the multi-dimensional restrictions about "what cores are important to which groups" and "wha,t cores are so when looking from which groups". As a new a,pproach, we model relationship between cores by a new core-community graph, and ca,lculate ranks of the cores there. Effects of our method are demonstrated by using all web sites under a university's domain.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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