Advances in Knowledge Discovery and Data Mining
5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings

David Cheung, Graham J. Williams, Qing Li

Items from 1 to 6 out of 6 results

chapter

Mining Optimal Class Association Rule Set

Jiuyong Li, Hong Shen, Rodney Topor

Lecture Notes in Computer Science > Advances in Knowledge Discovery and Data Mining > Association Mining > 364-375

We define an optimal class association rule set to be the minimum rule set with the same prediction power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We...

chapter

Generating Frequent Patterns with the Frequent Pattern List

Fan-Chen Tseng, Ching-Chi Hsu

Lecture Notes in Computer Science > Advances in Knowledge Discovery and Data Mining > Association Mining > 376-386

The generation of frequent patterns (or frequent itemsets) has been studied in various areas of data mining. Most of the studies take the Aprioribased generation-and-test approach, which is computationally costly in the generation of candidate frequent patterns. Methods like frequent pattern trees has been utilized to avoid candidate set generation, but they work with more complicated data structures...

chapter

User-Defined Association Mining

Ke Wang, Yu He

Lecture Notes in Computer Science > Advances in Knowledge Discovery and Data Mining > Association Mining > 387-399

Discovering interesting associations of events is an important data mining task. In many real applications, the notion of association, which defines how events are associated, often depends on the particular application and user requirements. This motivates the need for a general framework that allows the user to specify the notion of association of his/her own choices. In this paper we present such...

chapter

Direct and Incremental Computing of Maximal Covering Rules

Marzena Kryszkiewicz

Lecture Notes in Computer Science > Advances in Knowledge Discovery and Data Mining > Association Mining > 400-405

In the paper we consider the knowledge in the form of association rules. The consequents derivable from the given set of association rules constitute the theory for this rule set. We apply maximal covering rules as a concise representation of the theory. We prove that maximal covering rules have precisely computable values of support and confidence, though the theory can contain rules for which these...

chapter

Towards Efficient Data Re-mining (DRM)

Jiming Liu, Jian Yin

Lecture Notes in Computer Science > Advances in Knowledge Discovery and Data Mining > Association Mining > 406-412

The problem that we tackle here is a practical one: When users interactively mine association rules, it is often the case that they have to continuously tune two thresholds: minimum support and minimum confidence, which describe the users’ changing requirements. In this paper, we present an efficient data re-mining (DRM) technique for updating previously discovered association rules in light of threshold...

chapter

Data Allocation Algorithm for Parallel Association Rule Discovery

Anna M. Manning, John A. Keane

Lecture Notes in Computer Science > Advances in Knowledge Discovery and Data Mining > Association Mining > 413-420

Association rule discovery techniques have gradually been adapt-ed to parallel systems in order to take advantage of the higher speed and greater storage capacity that they offer. The transition to a distributed memory system requires the partitioning of the database among the processors, a procedure that is generally carried out indiscriminately. However, for some techniques the nature of the database...

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Advances in Knowledge Discovery and Data Mining
5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings

Mining Optimal Class Association Rule Set

Generating Frequent Patterns with the Frequent Pattern List

User-Defined Association Mining

Direct and Incremental Computing of Maximal Covering Rules

Towards Efficient Data Re-mining (DRM)

Data Allocation Algorithm for Parallel Association Rule Discovery

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INFONA - science communication portal

Advances in Knowledge Discovery and Data Mining 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings $("#expandableTitles").expandable();

Mining Optimal Class Association Rule Set

Generating Frequent Patterns with the Frequent Pattern List

User-Defined Association Mining

Direct and Incremental Computing of Maximal Covering Rules

Towards Efficient Data Re-mining (DRM)

Data Allocation Algorithm for Parallel Association Rule Discovery

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Advances in Knowledge Discovery and Data Mining
5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 Proceedings