@inproceedings{1e097d708d8340aeb196a629f2ab7696,
title = "Fast frequent itemset mining using compressed data representation",
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.",
keywords = "Association Rules, Data Mining, Frequent Itemsets, Knowledge Discovery",
author = "Gopalan, {Raj P.} and Sucahyo, {Yudho Giri}",
year = "2003",
language = "English",
isbn = "0889863415",
series = "IASTED International Multi-Conference on Applied Informatics",
pages = "1203--1208",
booktitle = "21st IASTED International Multi-Conference on Applied Informatics",
note = "21st IASTED International Multi-Conference on Applied Informatics ; Conference date: 10-02-2003 Through 13-02-2003",
}