Article

Support envelopes: a technique for exploring the structure of association patterns

Authors:

Michael Steinbach,

Vipin KumarAuthors Info & Claims

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 296 - 305

https://doi.org/10.1145/1014052.1014086

Published: 22 August 2004 Publication History

Abstract

This paper introduces support envelopes---a new tool for analyzing association patterns---and illustrates some of their properties, applications, and possible extensions. Specifically, the support envelope for a transaction data set and a specified pair of positive integers (m,n) consists of the items and transactions that need to be searched to find any association pattern involving m or more transactions and n or more items. For any transaction data set with M transactions and N items, there is a unique lattice of at most M*N support envelopes that captures the structure of the association patterns in that data set. Because support envelopes are not encumbered by a support threshold, this support lattice provides a complete view of the association structure of the data set, including association patterns that have low support. Furthermore, the boundary of the support lattice---the support boundary---has at most min(M,N) envelopes and is especially interesting since it bounds the maximum sizes of potential association patterns---not only for frequent, closed, and maximal itemsets, but also for patterns, such as error-tolerant itemsets, that are more general. The association structure can be represented graphically as a two-dimensional scatter plot of the (m,n) values associated with the support envelopes of the data set, a feature that is useful in the exploratory analysis of association patterns. Finally, the algorithm to compute support envelopes is simple and computationally efficient, and it is straightforward to parallelize the process of finding all the support envelopes.

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Yu XLi YWang H(2015)Mining Approximate Frequent Patterns from Noisy DatabasesProceedings of the 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA)10.1109/BWCCA.2015.29(400-403)Online publication date: 4-Nov-2015
https://dl.acm.org/doi/10.1109/BWCCA.2015.29
Zhang XDeng Z(2015)Mining summarization of high utility itemsetsKnowledge-Based Systems10.1016/j.knosys.2015.04.00484:C(67-77)Online publication date: 1-Aug-2015
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Lucchese COrlando SPerego R(2014)A Unifying Framework for Mining Approximate Top- $k$ Binary PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2013.18126:12(2900-2913)Online publication date: Dec-2014
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Index Terms

Support envelopes: a technique for exploring the structure of association patterns
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2004

874 pages

ISBN:1581138881

DOI:10.1145/1014052

General Chairs:
Won Kim
Cyber Database Solutions
,
Ronny Kohavi
Amazon.com
,
Program Chairs:
Johannes Gehrke
Cornell University
,
William DuMouchel
AT&T Labs Research

Copyright © 2004 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 22 August 2004

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KDD04: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 22 - 25, 2004

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Yu XLi YWang H(2015)Mining Approximate Frequent Patterns from Noisy DatabasesProceedings of the 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA)10.1109/BWCCA.2015.29(400-403)Online publication date: 4-Nov-2015
https://dl.acm.org/doi/10.1109/BWCCA.2015.29
Zhang XDeng Z(2015)Mining summarization of high utility itemsetsKnowledge-Based Systems10.1016/j.knosys.2015.04.00484:C(67-77)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.knosys.2015.04.004
Lucchese COrlando SPerego R(2014)A Unifying Framework for Mining Approximate Top- $k$ Binary PatternsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2013.18126:12(2900-2913)Online publication date: Dec-2014
https://doi.org/10.1109/TKDE.2013.181
Hamrouni TBen Yahia SMephu Nguifo E(2013)Looking for a structural characterization of the sparseness measure of (frequent closed) itemset contextsInformation Sciences: an International Journal10.1016/j.ins.2012.08.005222(343-361)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.ins.2012.08.005
Xiang YJin RFuhry DDragan F(2011)Summarizing transactional databases with overlapped hyperrectanglesData Mining and Knowledge Discovery10.1007/s10618-010-0203-923:2(215-251)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1007/s10618-010-0203-9
Pandey GAtluri GSteinbach MMyers CKumar VElder JFogelman FFlach PZaki M(2009)An association analysis approach to biclusteringProceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1557019.1557095(677-686)Online publication date: 28-Jun-2009
https://dl.acm.org/doi/10.1145/1557019.1557095
Xiang YJin RFuhry DDragan FLi YLiu BSarawagi S(2008)Succinct summarization of transactional databasesProceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1401890.1401981(758-766)Online publication date: 24-Aug-2008
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Gupta RFang GField BSteinbach MKumar VLi YLiu BSarawagi S(2008)Quantitative evaluation of approximate frequent pattern mining algorithmsProceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/1401890.1401930(301-309)Online publication date: 24-Aug-2008
https://dl.acm.org/doi/10.1145/1401890.1401930
Cheng HYu PHan J(2008)Approximate Frequent Itemset Mining In the Presence of Random NoiseSoft Computing for Knowledge Discovery and Data Mining10.1007/978-0-387-69935-6_15(363-389)Online publication date: 2008
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Huang JDai BChen M(2007)TwainACM Transactions on Knowledge Discovery from Data10.1145/1267066.12670691:2(8-es)Online publication date: 1-Aug-2007
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