article

Discovery in multi-attribute data with user-defined constraints

Authors:

Chang-Shing Perng,

Joseph L. HellersteinAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 4, Issue 1

Pages 56 - 64

https://doi.org/10.1145/568574.568583

Published: 01 June 2002 Publication History

Abstract

There has been a growing interest in mining frequent itemsets in relational data with multiple attributes. A key step in this approach is to select a set of attributes that group data into transactions and a separate set of attributes that labels data into items. Unsupervised and unrestricted mining, however, is stymied by the combinatorial complexity and the quantity of patterns as the number of attributes grows. In this paper, we focus on leveraging the semantics of the underlying data for mining frequent itemsets. For instance, there are usually taxonomies in the data schema and functional dependencies among the attributes. Domain knowledge and user preferences often have the potential to significantly reduce the exponentially growing mining space. These observations motivate the design of a user-directed data mining framework that allows such domain knowledge to guide the mining process and control the mining strategy. We show examples of tremendous reduction in computation by using domain knowledge in mining relational data with multiple attributes.

References

[1]

R. Aggarwal, C. Aggarwal, and V. Parsad. Depth first generation of long patterns. In Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD), 2000.

Digital Library

[2]

R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of Very Large Database. (VLDB), pages 207-216, 1993.

Digital Library

[3]

R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of Very Large Database (VLDB), 1994.

Digital Library

[4]

R. Bayardo. Efficiently mining long patterns from database. In Int. Conf. Management of Data (SIGMOD), pages 85-93, 1998.

Digital Library

[5]

J. Deogun, V. Raghavan, A. Sarkar, and H. Sever. Data mining: Research trends, challenges, and applications, 1997.

[6]

H. B. Enderton. A Mathematical Introduction to Logic. Academic Press, 2nd edition, December 2000.

[7]

G. Grahne, L. Lakshmanan, X. Wang, and M. Xie. On dual mining: From patterns to circumstances, and back. In Int. Conf. Data Engineering (ICDE), pages 195-204, 2001.

Digital Library

[8]

J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In Proc. of Very Large Database (VLDB), 1995.

Digital Library

[9]

J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Int. Conf. Management of Data (SIGMOD), 2000.

Digital Library

[10]

J. Hipp, A. Myka, R. Wirth, and U. Guntzer. A new algorithm for faster mining of generalized association rules. In Proc. 2nd PKKD, 1998.

Digital Library

[11]

R. Ng, L. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Int. Conf. Management of Data (SIGMOD), pages 13-24, 1998.

Digital Library

[12]

B. Padmanabhan and A. Tuzhilin. Unexpectedness as a measure of interestingness in knowledge discovery, 1999.

[13]

C.-S. Perng, H. Wang, S. Ma, and J. L. Hellerstein. Farm: A framework for exploring mining spaces with multiple attributes. In IEEE Int. Conf. on Data Mining(ICDM), 2001.

Digital Library

[14]

J. A. Robinson. A machine-oriented logic based on the resolution principle. Journal of the ACM, 12:23-41, 1965.

Digital Library

[15]

A. Silberschatz and A. Tuzhilin. What makes patterns interesting in knowledge discovery systems. IEEE Trans. On Knowledge And Data Engineering, 8:970-974, 1996.

Digital Library

[16]

R. Srikant and R. Agrawal. Mining generalized association rules. In Proc. of Very Large Database (VLDB), pages 407-419, 1995.

Digital Library

[17]

R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD), pages 67-93, 1997.

[18]

A. Tarski. A lattice-theoretical fixpoint theorem and its applications. Pacific J. Math., pages 253-309, 1955.

[19]

M. H. van Emden and R. A. Kowalski. The semantics of predicate logic as a programming language. J. ACM, pages 733-742, October 1976.

Digital Library

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Esposito RMeo RBotta M(2018)Answering constraint-based mining queries on itemsets using previous materialized resultsJournal of Intelligent Information Systems10.1007/s10844-006-5453-z26:1(95-111)Online publication date: 28-Dec-2018
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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 4, Issue 1

June 2002

75 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/568574

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Copyright © 2002 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2002

Published in SIGKDD Volume 4, Issue 1

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Cited By

Kamley SJaloree SThakur RSaxena K(2021)COVID‐19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data Mining TechniquesComputational Intelligence and Healthcare Informatics10.1002/9781119818717.ch11(193-213)Online publication date: 25-Oct-2021
https://doi.org/10.1002/9781119818717.ch11
Esposito RMeo RBotta M(2018)Answering constraint-based mining queries on itemsets using previous materialized resultsJournal of Intelligent Information Systems10.1007/s10844-006-5453-z26:1(95-111)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-006-5453-z
Sug H(2011)An Effective Method to Find Better Data Mining Model Using Inferior Class OversamplingConvergence and Hybrid Information Technology10.1007/978-3-642-24106-2_73(576-583)Online publication date: 2011
https://doi.org/10.1007/978-3-642-24106-2_73
Morantz B(2009)Constrained Data MiningEncyclopedia of Data Warehousing and Mining, Second Edition10.4018/978-1-60566-010-3.ch048(301-306)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-010-3.ch048
Punuru JChen J(2007)Learning for Semantic Classification of Conceptual TermsProceedings of the 2007 IEEE International Conference on Granular Computing10.1109/GRC.2007.92Online publication date: 2-Nov-2007
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Gallo AEsposito RMeo RBotta M(2007)INCREMENTAL EXTRACTION OF ASSOCIATION RULES IN APPLICATIVE DOMAINSApplied Artificial Intelligence10.1080/0883951070125248621:4-5(297-315)Online publication date: 1-Apr-2007
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Gallo AMeo R(2007)Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed ItemsetsKnowledge Discovery in Inductive Databases10.1007/978-3-540-75549-4_7(97-115)Online publication date: 2007
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Gallo AMeo R(2006)Using a reinforced concept lattice to incrementally mine association rules from closed itemsetsProceedings of the 5th international conference on Knowledge discovery in inductive databases10.5555/1777194.1777203(97-115)Online publication date: 18-Sep-2006
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Jin RSinha KAgrawal GBouali FKhan LMasseglia F(2005)A framework to support multiple query optimization for complex mining tasksProceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data10.1145/1133890.1133893(23-32)Online publication date: 21-Aug-2005
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Gallo AEsposito RMeo RBotta M(2005)Optimization of association rules extraction through exploitation of context dependent constraintsProceedings of the 9th conference on Advances in Artificial Intelligence10.1007/11558590_26(258-269)Online publication date: 21-Sep-2005
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