An efficient multi-relational naïve Bayesian classifier based on semantic relationship graph

Hongyan Liu, Xiaoxin Yin, Jiawei Han

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

Abstract

Classification is one of the most popular data mining tasks with a wide range of applications, and lots of algorithms have been proposed to build accurate and scalable classifiers. Most of these algorithms only take a single table as input, whereas in the real world most data are stored in multiple tables and managed by relational database systems. As transferring data from multiple tables into a single one usually causes many problems, development of multi-relational classification algorithms becomes important and attracts many researchers' interests. Existing works about extending Naïve Bayes to deal with multi-relational data either have to transform data stored in tables to mainmemory Prolog facts, or limit the search space to only a small subset of real world applications. In this work, we aim at solving these problems and building an efficient, accurate Naïve Bayesian classifier to deal with data in multiple tables directly. We propose an algorithm named Graph-NB, which upgrades Naïve Bayesian classifier to deal with multiple tables directly. In order to take advantage of linkage relationships among tables, and treat different tables linked to the target table differently, a semantic relationship graph is developed to describe the relationship and to avoid unnecessary joins. Furthermore, to improve accuracy, a pruning strategy is given to simplify the graph to avoid examining too many weakly linked tables. Experimental study on both realworld and synthetic databases shows its high efficiency and good accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th International Workshop on Multi-Relational Data Mining, MRDM 2005 - 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2005
EditorsHendrik Blockeel, Saso Dzeroski
PublisherAssociation for Computing Machinery
Pages39-48
Number of pages10
ISBN (Electronic)1595932127, 9781595932129
DOIs
StatePublished - Aug 21 2005
Event4th International Workshop on Multi-Relational Data Mining, MRDM 2005 - Chicago, United States
Duration: Aug 21 2005 → …

Publication series

NameProceedings of the 4th International Workshop on Multi-Relational Data Mining, MRDM 2005 - 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2005

Other

Other4th International Workshop on Multi-Relational Data Mining, MRDM 2005
Country/TerritoryUnited States
CityChicago
Period8/21/05 → …

Keywords

  • Classification
  • Data Mining
  • Naïve Bayes

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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