Maximal Association Rules: a New Tool for Mining for Keyword cooccurrences in Document Collections

Ronen Feldman, Yonatan Aumann, Amihood Amir, Amir Zilberstein, Willi Kloesgen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

42 Scopus citations

Abstract

Knowledge Discovery in Databases (KDD) focuses on the computerized exploration of large amounts of data and on the discovery of interesting patterns within them. While most work on KDD has been concerned with structured databases, there has been little work on handling the huge amount of information that is available only in unstructured document collections. This paper describes a new method for computing co-occurrence frequencies of the various keywords labeling the documents. This method is based on computing maximal association rules. Regular associations are based on the notion of frequent sets: sets of attributes, which appear in many records. In analogy, maximal associations are based on the notion of frequent maximal sets. Conceptually, a frequent maximal set is a set of attributes, which appear alone, or maximally, in many records. For the definition of "maximality"we use an underlying taxonomy, T, of the attributes. This allows us to obtain the "interesting"correlations between attributes from different categories. Frequent maximal sets are useful for efficiently finding association rules that include negated attributes. We provide an experimental evaluation of our methodology on the Reuters-21578 document collection.

Original languageAmerican English
Title of host publicationProceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997
EditorsDavid Heckerman, Heikki Mannila, Daryl Pregibon, Ramasamy Uthurusamy
PublisherAAAI Press
Pages167-170
Number of pages4
ISBN (Electronic)1577350278, 9781577350279
StatePublished - 1997
Externally publishedYes
Event3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997 - Newport Beach, United States
Duration: 14 Aug 199717 Aug 1997

Publication series

NameProceedings - 3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997

Conference

Conference3rd International Conference on Knowledge Discovery and Data Mining, KDD 1997
Country/TerritoryUnited States
CityNewport Beach
Period14/08/9717/08/97

Bibliographical note

Publisher Copyright:
Copyright © 1997, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.

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