@inproceedings{4c9d670b064e4ef2a86df0ec2437c4e5,
title = "Using Random walks for mining web document associations",
abstract = "World Wide Web has emerged as a primetry means for storing and structuring information. In this paper, we present a framework for mining implicit associations among Web documents. We focus on the following problem: {\textquotedblleft}For a given set of seed URLs, find a list of Web pages which reflect the association among these seeds.{\textquotedblright} In the proposed framework, associations of two documents are induced by the connectivity and linking path length. Based on this framework, we have developed a random walk-hased Web mining technique and validated it by experiments on real Web data. In this paper, we also discuss the extension of the algorithm for considering document contents.",
author = "Kasim Candan and Li, \{Wen Syan\}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2000.; 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 ; Conference date: 18-04-2000 Through 20-04-2000",
year = "2000",
language = "English (US)",
isbn = "3540673822",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "294--305",
editor = "Chen, \{Arbee L.P.\} and Takao Terano and Huan Liu",
booktitle = "Knowledge Discovery and Data Mining",
}