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Abstract: Discovery of frequently occurring subsets of items, called itemsets, is the core of many data mining methods. Most of the previous studies adopt Apriori-like algorithms, which iteratively generate candidate itemsets and check their occurrence frequencies in the database. These approaches suffer from serious costs of repeated passes over the analyzed database. To address this problem, we propose a novel method, called HASH-MINE, for reducing database activity of frequent itemset discovery... (Update)
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BibTeX entry: (Update)
@misc{ zakrzewicz-hashmine,
author = "Marek Wojciechowski Maciej Zakrzewicz",
title = "HASH-MINE: A New Framework for Discovery of Frequent Itemsets",
url = "citeseer.ist.psu.edu/692235.html" }
Citations (may not include all citations):921 Mining Association Rules Between Sets of Items in Large Data.. - Agrawal, Imielinski et al. - 1993
910 Fast Algorithms for Mining Association Rules - Agrawal, Srikant - 1994
340 Mining Sequential Patterns - Agrawal, Srikant - 1995
242 Dynamic Itemset Counting and Implication Rules for Market Ba.. - Brin, Motwani et al. - 1997
164 An Efficient Algorithm for Mining Association Rules in Large.. (context) - Savasere, Omiecinski et al. - 1995
125 An Effective Hash-Based Algorithm for Mining Association Rul.. - Park, Chen et al. - 1995
44 The Quest Data Mining System - Agrawal, Mehta et al. - 1996
35 Hypergraph Based Clustering in HighDimensional Data Sets: A .. - Han, Karypis et al. - 1998
Documents on the same site (http://www.cs.put.poznan.pl/mwojciechowski/papers.htm): More
Itemset Materializing for Fast Mining of Association Rules - Wojciechowski, Zakrzewicz (Correct)
Discovering Frequent Episodes in Sequences of Complex Events - Wojciechowski (Correct)
Data Access Paths for Frequent Itemsets Discovery - Wojciechowski, Zakrzewicz (Correct)
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