Article

Mining frequent closed cubes in 3D datasets

Authors:

Anthony K. H. TungAuthors Info & Claims

VLDB '06: Proceedings of the 32nd international conference on Very large data bases

Pages 811 - 822

Published: 01 September 2006 Publication History

Abstract

In this paper, we introduce the concept of frequent closed cube (FCC), which generalizes the notion of 2D frequent closed pattern to 3D context. We propose two novel algorithms to mine FCCs from 3D datasets. The first scheme is a Representative Slice Mining (RSM) framework that can be used to extend existing 2D FCP mining algorithms for FCC mining. The second technique, called CubeMiner, is a novel algorithm that operates on the 3D space directly. We have implemented both schemes, and evaluated their performance on both real and synthetic datasets. The experimental results show that the RSM-based scheme is efficient when one of the dimensions is small, while CubeMiner is superior otherwise.

References

[1]

{1} R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of 20th International Conference on Very Large Data Bases (VLDB'94), pages 487-499, Santiago, Chile, September 1994.]]

Digital Library

[2]

{2} R. Agrawal and R. Srikant. Mining sequential patterns. In ICDE'95, pages 3-14, Taibei, Taiwan, March 1995.]]

Digital Library

[3]

{3} J. Besson, C. Robardet, and J.-F. Boulicaut. Constraint-based mining of formal concepts in transactional data. In PAKDD'04, pages 615-624, Sydney, Australia, May 2004.]]

[4]

{4} D. Burdick, M. Calimlim, and J. Gehrke. Mafia: A maximal frequent itemset algorithm for transactional databases. In ICDE'01, pages 443-452, Heidelberg, Germany, April 2001.]]

Digital Library

[5]

{5} J. Han, G. Dong, and Y. Yin. Efficient mining of partial periodic patterns in time series database. In ICDE'99, pages 106-115, Sydney, Australia, April 1999.]]

Digital Library

[6]

{6} D. Jiang, J. Pei, M. Ramanathany, C. Tang, and A. Zhang. Mining coherent gene clusters from gene-sample-time microarray data. In SIGKDD'04, pages 430-439, Seattle, Washington, USA, August 2004.]]

Digital Library

[7]

{7} H. Mannila, H. Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. In Data Mining and Knowledge Discovery, pages 259-289, 1997.]]

Digital Library

[8]

{8} F. Pan, G. Cong, and A. K. H. Tung. Carpenter: Finding closed patterns in long biological datasets. In SIGKDD'03, pages 637-642, Washington, DC, USA, August 2003.]]

Digital Library

[9]

{9} F. Pang, A. K. H. Tung, G. Cong, and X. Xu. Cobbler: Combining column and row enumeration for closed pattern discovery. In SSDBM, pages 21-30, Santorini Island, Greece, June 2004.]]

Digital Library

[10]

{10} N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. In ICDT'99, pages 398-416, Jerusalem, Israel, January 1999.]]

Digital Library

[11]

{11} J. Pei, J. Han, and R. Mao. Closet: An efficient algorithm for mining frequent closed itemsets. In Proceedings of 2000 ACMSIGMOD Int. Workshop Data Mining and Knowledge Discovery, May 2000.]]

[12]

{12} H. Pinto, J. Han, J. Pei, K. Wang, Q. Chen, and U. Dayal. Multi-dimensional sequential pattern mining. In CIKM'01, pages 81-88, Atlanta, Georgia, November 2001.]]

Digital Library

[13]

{13} S. Ramaswamy, S. Mahajan, and A. Silberschatz. On the discovery of interesting patterns in association rules. In VLDB'98, pages 368-379, New York City, USA, August 1998.]]

Digital Library

[14]

{14} P. Shenoy, J. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, and D. Shah. Turbo-charging vertical mining of large databases. In SIGMOD'00, pages 22-23, Dallas, TX, May 2000.]]

Digital Library

[15]

{15} P. T. Spellman, G. Sherlock, and etc. Comprehensive identication of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. In Molecular Biology of the Cell, pages 3273-3297, December 1998.]]

[16]

{16} J. Wang, J. Han, and J. Pei. Closet+: Searching for the best strategies for mining frequent closed itemsets. In SIGKDD'03, pages 236-245, Washington, D.C., August 2003.]]

Digital Library

[17]

{17} M. Zaki and C. Hsiao. CHARM: An efficient algorithm for closed association rule mining. In SDM'02, Arlington, VA, USA, April 2002.]]

[18]

{18} M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In KDD'97, pages 283-286, Newport Beach, CA, August 1997.]]

[19]

{19} L. Zhao and M. J. Zaki. tricluster: An effective algorithm for mining coherent clusters in 3d microarray data. In SIGMOD'05, pages 694-705, Baltimore, Maryland, USA, June 2005.]]

Digital Library

Cited By

Henriques RMadeira S(2018)Triclustering Algorithms for Three-Dimensional Data AnalysisACM Computing Surveys10.1145/319583351:5(1-43)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3195833
Savio MSankar ANataraj RRangan PJayaraman B(2014)Removal of Mirror Bicliques from a Symmetric MatrixProceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing10.1145/2660859.2660955(1-4)Online publication date: 10-Oct-2014
https://dl.acm.org/doi/10.1145/2660859.2660955
Poelmans JKuznetsov SIgnatov DDedene G(2013)ReviewExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.05.00740:16(6601-6623)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1016/j.eswa.2013.05.007
Show More Cited By

Index Terms

Mining frequent closed cubes in 3D datasets
1. Information systems

Recommendations

Closed frequent similar pattern mining

The concept of closed frequent similar pattern mining is introduced.Several lemmas to prune the search space are introduced and proved.A novel closed frequent similar pattern mining algorithm (CFSP-Miner), is proposed.CFSP-Miner is more efficient than ...
A method for mining top-rank-k frequent closed itemsets
Collective intelligent information and database systems

Mining frequent closed itemsets (FCIs) is important in mining non-redundant (minimal) association rules. Therefore, many algorithms have been developed for mining FCIs with reduced mining time and memory usage. For mining FCIs, algorithms use the minimum ...
An Efficient Algorithm for Frequent Closed Itemsets Mining
CSSE '08: Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04

Efficient algorithms for mining frequent itemsets are crucial for mining association rules. Most existing work focuses on mining all frequent itemsets. However, since any subset of a frequent set also is frequent, it is sufficient to mine the set of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

VLDB '06: Proceedings of the 32nd international conference on Very large data bases

September 2006

1269 pages

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data
K.I.S.S. SIG on Databases
AJU Information Technology Co., Ltd
US Army ITC-PAC Asian Research Office
Google Inc.
The Database Society of Japan
Samsung SOS
Advanced Information Technology Research Center
Naver
Microsoft: Microsoft
Korea Info Sci Society: Korea Information Science Society
SK telecom
Systems Applications Products
ORACLE: ORACLE
International Business Management
Air Force Office of Scientific Research/Asian Office of Aerospace R&D
Kosef
Kaist
LG Electronics
CCF-DBS

Publisher

VLDB Endowment

Publication History

Published: 01 September 2006

Check for updates

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
479
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Henriques RMadeira S(2018)Triclustering Algorithms for Three-Dimensional Data AnalysisACM Computing Surveys10.1145/319583351:5(1-43)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3195833
Savio MSankar ANataraj RRangan PJayaraman B(2014)Removal of Mirror Bicliques from a Symmetric MatrixProceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing10.1145/2660859.2660955(1-4)Online publication date: 10-Oct-2014
https://dl.acm.org/doi/10.1145/2660859.2660955
Poelmans JKuznetsov SIgnatov DDedene G(2013)ReviewExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.05.00740:16(6601-6623)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1016/j.eswa.2013.05.007
Trabelsi CJelassi NBen Yahia S(2012)Scalable mining of frequent tri-concepts from folksonomiesProceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II10.1007/978-3-642-30220-6_20(231-242)Online publication date: 29-May-2012
https://dl.acm.org/doi/10.1007/978-3-642-30220-6_20
Ignatov DKuznetsov SMagizov RZhukov L(2011)From triconcepts to triclustersProceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing10.5555/2026782.2026832(257-264)Online publication date: 25-Jun-2011
https://dl.acm.org/doi/10.5555/2026782.2026832
Missaoui RKwuida L(2011)Mining triadic association rules from ternary relationsProceedings of the 9th international conference on Formal concept analysis10.5555/2008557.2008575(204-218)Online publication date: 2-May-2011
https://dl.acm.org/doi/10.5555/2008557.2008575
Georgii ETsuda KSchölkopf BDing CLi T(2009)Multi-way set enumeration in real-valued tensorsProceedings of the 2nd Workshop on Data Mining using Matrices and Tensors10.1145/1581114.1581118(1-10)Online publication date: 28-Jun-2009
https://dl.acm.org/doi/10.1145/1581114.1581118
Cerf LBesson JRobardet CBoulicaut J(2009)Closed patterns meet n-ary relationsACM Transactions on Knowledge Discovery from Data10.1145/1497577.14975803:1(1-36)Online publication date: 23-Mar-2009
https://dl.acm.org/doi/10.1145/1497577.1497580
Boulicaut JBesson J(2008)Actionability and formal conceptsProceedings of the 6th international conference on Formal concept analysis10.5555/1787746.1787748(14-31)Online publication date: 25-Feb-2008
https://dl.acm.org/doi/10.5555/1787746.1787748

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents