Discovering Imperceptible Associations Based on Interestingness: A Utility-Oriented Data Mining

Discovering Imperceptible Associations Based on Interestingness: A Utility-Oriented Data Mining

This article proposes an innovative utility sentient approach for the mining of interesting association patterns from transaction databases. First, frequent patterns are discovered from the transaction database using the FP-Growth algorithm. From the frequent patterns mined, this approach extracts n...

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Bibliographic Details
Main Authors: S Shankar, T Purusothaman
Format: Article
Language:English
Published: Ubiquity Press 2010-02-01
Series:Data Science Journal
Subjects:
Data Mining
Frequent Patterns
Association Rules
FP-Growth
Economic Utility
Weight
Significance
Interestingness
Subjective Interestingness
Online Access:http://datascience.codata.org/articles/124

Internet

http://datascience.codata.org/articles/124

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