DMajor—Application Programming Interface for Database Mining

Authors:
Tomasz Imieliński;Aashu Virmani;Amin Abdulghani
Affiliations:
Rutgers University, Department of Computer Science, New Brunswick, N.J. 08903 USA. imielins@cs.rutgers.edu;Rutgers University, Department of Computer Science, New Brunswick, N.J. 08903 USA. avirmani@cs.rutgers.edu;Rutgers University, Department of Computer Science, New Brunswick, N.J. 08903 USA. aminabdu@cs.rutgers.edu
Venue:
Data Mining and Knowledge Discovery
Year:
1999

Citing 11
Cited 5

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An effective hash-based algorithm for mining association rules

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A database perspective on knowledge discovery

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Dynamic itemset counting and implication rules for market basket data

SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Metaqueries for data mining

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Query flocks: a generalization of association-rule mining

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DBMiner: interactive mining of multiple-level knowledge in relational databases

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MSQL: A Query Language for Database Mining

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Fast Algorithms for Mining Association Rules in Large Databases

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An Efficient Algorithm for Mining Association Rules in Large Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules

VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases

Is pushing constraints deeply into the mining algorithms really what we want?: an alternative approach for association rule mining

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Cubegrades: Generalizing Association Rules

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Data Mining of Association Rules and the Process of Knowledge Discovery in Databases

Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining

PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A relational query primitive for constraint-based pattern mining

Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases

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Abstract

In the process of rule generation from databases, the volume ofgenerated rules often greatly exceeds the size of the underlyingdatabase. Typically only a small fraction of that large volume ofrules is of any interest to the user. We believe that the mainchallenge facing database mining is what to do with the rulesafter having generated them. Rule post-processing involvesselecting rules which are relevant or interesting, buildingapplications which use the rules and finally, combining rules togetherto form a larger and more meaningful statements. In this paper wepropose an application programming interface which enables fasterdevelopment of applications which rely on rules. We also provide arule query language which allows both selective rule generation aswell as retrieval of selected categories of rules from thepre-generated rule collections.