Fast frequent itemset mining using compressed data representation

Raj P. Gopalan, Yudho Giri Sucahyo

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

8 Citations (Scopus)

Abstract

Discovering association rules by identifying relationships among sets of items in a transaction database is an important problem in Date Mining. Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we describe a more efficient algorithm for mining complete frequent itemsets from typical data sets. We use a compressed prefix tree and our algorithm extracts the frequent itemsets directly from the tree. We present performance comparisons of our algorithm against the fastest Apriori algorithm, Eclat, and FP-Growth. These results show that our algorithm outperforms other algorithms on several widely used test data sets.

Original languageEnglish
Title of host publication21st IASTED International Multi-Conference on Applied Informatics
Pages1203-1208
Number of pages6
Publication statusPublished - 1 Dec 2003
Event21st IASTED International Multi-Conference on Applied Informatics - Innsbruck, Austria
Duration: 10 Feb 200313 Feb 2003

Publication series

NameIASTED International Multi-Conference on Applied Informatics
Volume21

Conference

Conference21st IASTED International Multi-Conference on Applied Informatics
Country/TerritoryAustria
CityInnsbruck
Period10/02/0313/02/03

Keywords

  • Association Rules
  • Data Mining
  • Frequent Itemsets
  • Knowledge Discovery

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