Mining Non-redundant Frequent Patterns in Taxonomy Datasets using Concept Lattices
Vijaya R Prakash, A Govardhan and Ssvn Sarma. Article: Mining Non-redundant Frequent Patterns in Taxonomy Datasets using Concept Lattices. International Journal of Applied Information Systems 3(9):1-6, August 2012. BibTeX
@article{key:article, author = "R. Vijaya Prakash and A. Govardhan and Ssvn Sarma", title = "Article: Mining Non-redundant Frequent Patterns in Taxonomy Datasets using Concept Lattices", journal = "International Journal of Applied Information Systems", year = 2012, volume = 3, number = 9, pages = "1-6", month = "August", note = "Published by Foundation of Computer Science, New York, USA" }
Abstract
In general frequent itemsets are generated from large data sets by applying various association rule mining algorithms, these produce many redundant frequent itemsets. In this paper we proposed a new framework for Non-redundant frequent itemset generation using closed frequent itemsets without lose of information on Taxonomy Datasets using concept lattices.
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Keywords
Non Redundant, Frequent Patterns, Concept Lattice, Association Rules, Itemset