Estimating joint probabilities from marginal ones

Tao Li, Shenghuo Zhu, Mitsunori Ogihara, Yinhe Cheng

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

3 Scopus citations


Estimating joint probabilities plays an important role in many data mining and machine learning tasks. In this paper we introduce two methods, minAB and prodAB, to estimate joint probabilities. Both methods are based on a light-weight structure, partition support. The core idea is to maintain the partition support of itemsets over logically disjoint partitions and then use it to estimate joint probabilities of itemsets of higher cardinalitiess. We present extensive mathematical analyses on both methods and compare their performances on synthetic datasets. We also demonstrate a case study of using the estimation methods in Apriori algorithm for fast association mining. Moreover, we explore the usefulness of the estimation methods in other mining/learning tasks [9]. Experimental results show the effectiveness of the estimation methods.

Original languageEnglish (US)
Title of host publicationData Warehousing and Knowledge Discovery - 4th International Conference, DaWaK 2002, Proceedings
EditorsYahiko Kambayashi, Werner Winiwarter, Masatoshi Arikawa
PublisherSpringer Verlag
Number of pages11
ISBN (Print)3540441239, 9783540441236
StatePublished - 2002
Externally publishedYes
Event4th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2002 - Aix-en-Provence, France
Duration: Sep 4 2002Sep 6 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2454 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other4th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2002


  • Association mining
  • Estimation
  • Joint probability

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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