The item-set tree: A data structure for data mining

Alaaeldin Hafez, Jitender Deogun, Vijay V. Raghavan

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

21 Scopus citations


Enhancements in data capturing technology have lead to exponential growth in amounts of data being stored in information systems. This growth in turn has motivated researchers to seek new techniques for extraction of knowledge implicit or hidden in the data. In this paper, we motivate the need for an incremental data mining approach based on data structure called the itemset tree. The motivated approach is shown to be effective for solving problems related to efficiency of handling data updates, accuracy of data mining results, processing input transactions, and answering user queries. We present efficient algorithms to insert transactions into the item-set tree and to count frequencies of itemsets for queries about strength of association among items. We prove that the expected complexity of inserting a transaction is ≈ O(1), and that of frequency counting is O(n), where n is the cardinality of the domain of items.

Original languageEnglish (US)
Title of host publicationData Warehousing and Knowledge Discovery - 1st International Conference, DaWaK 1999, Proceedings
EditorsA. Min Tjoa, Mukesh Mohania
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540664580, 9783540664581
StatePublished - 1999
Event1st International Conference on Data Warehousing and Knowledge Discovery, DaWaK 1999 - Florence, Italy
Duration: Aug 30 1999Sep 1 1999

Publication series

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


Other1st International Conference on Data Warehousing and Knowledge Discovery, DaWaK 1999

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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