Exploring the power of heuristics and links in multi-relational data mining

Xiaoxin Yin, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Relational databases are the most popular repository for structured data, and are thus one of the richest sources of knowledge in the world. Because of the complexity of relational data, it is a challenging task to design efficient and scalable data mining approaches in relational databases. In this paper we discuss two methodologies to address this issue. The first methodology is to use heuristics to guide the data mining procedure, in order to avoid aimless, exhaustive search in relational databases. The second methodology is to assign certain property to each object in the database, and let different objects interact with each other along the links. Experiments show that both approaches achieve high efficiency and accuracy in real applications.

Original languageEnglish (US)
Title of host publicationFoundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings
Pages17-27
Number of pages11
DOIs
StatePublished - Jun 9 2008
Event17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008 - Toronto, Canada
Duration: May 20 2008May 23 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4994 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008
CountryCanada
CityToronto
Period5/20/085/23/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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