A mining method for linked Web pages using associated keyword space

Author

Yaguchi, Yuuichi ; Ohnishi, Hiroshi ; Mori, Satoshi ; Naruse, Keitaro ; Oka, Ryuichi ; Takahashi, Hironobu

Author_Institution

Aizu Univ.

fYear

2006

fDate

23-27 Jan. 2006

Lastpage

276

Abstract

We propose a novel method for mining knowledge from linked Web pages. Unlike most conventional methods for extracting knowledge from linked data, which are based on graph theory, the proposed method is based on our associated keyword space (ASKS), which is a nonlinear version of linear multidimensional scaling (MDS), such as quantification method type IV (Q-IV). We constructed a three-dimensional ASKS space using linked HTML data from the World Wide Web. Experimental results confirm that the performance of ASKS is superior to that of Q-IV for discriminating clusters in the space obtained. We also demonstrate a mining procedure realized by 1) finding subspaces obtained in terms of logical calculations between subspaces in an ASKS space and 2) detecting emerging spatial patterns with geometrical features

Keywords

Internet; data mining; hypermedia markup languages; HTML data; Web page; World Wide Web; associated keyword space; geometrical feature; knowledge extraction; linear multidimensional scaling; mining method; quantification method type IV; spatial pattern; Amplitude shift keying; Clustering algorithms; Data analysis; Data mining; Graph theory; HTML; Internet; Multidimensional systems; Web pages; Web sites;

fLanguage

English

Publisher

ieee

Conference_Titel

Applications and the Internet, 2006. SAINT 2006. International Symposium on

Conference_Location

Phoenix, AZ

Print_ISBN

0-7695-2508-3

Type

conf

DOI

10.1109/SAINT.2006.4

Filename

1581343