Probability logic modeling of knowledge discovery in databases

Jitender Deogun, Liying Jiang, Ying Xie, Vijay Raghavan

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

8 Scopus citations

Abstract

A Knowledge discovery in databases (KDD) system with probability deduction capability is expected to provide more information for decision making. Based on Bacchus probability logic and formal concept analysis, we propose a logic model for KDD with probability deduction. We use formal concept analysis within the semantics of probability logic to import the notion of concept into modeling of KDD. One of the most important features of a KDD system is its ability to discover previously unknown and potentially useful patterns. We formalize the definitions of previously unknown and potentially useful patterns.

Original languageEnglish (US)
Title of host publicationFoundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings
EditorsNing Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki
PublisherSpringer Verlag
Pages402-407
Number of pages6
ISBN (Print)3540202560, 9783540202561
DOIs
StatePublished - 2003
Event14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003 - Maebashi City, Japan
Duration: Oct 28 2003Oct 31 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2871
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003
CountryJapan
CityMaebashi City
Period10/28/0310/31/03

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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