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A Novel preprocessing Algorithm for Frequent Pattern Mining in Multidatasets
K.Duraiswamy, Jayanthi Balasubramaniam
Pages - 111 - 118     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 2   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
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KEYWORDS
Multiple-Level, CCB-Tree, Association Rule, Minimum Support, Frequent Patterns
ABSTRACT
In many database applications, information stored in a database has a built-in hierarchy consisting of multiple levels of concepts. In such a database users may want to find out association rules among items only at the same levels. This task is called multiple-level association rule mining. However, mining frequent patterns at multiple levels may lead to the discovery of more specific and concrete knowledge from data. Initial step to find frequent pattern is to preprocess the multidataset to find the large 1 frequent pattern for all levels. In this research paper, we introduce a new algorithm, called CCB-tree i.e., Category-Content-Brand tree is developed to mine Large 1 Frequent pattern for all levels of abstraction. The proposed algorithm is a tree based structure and it first constructs the tree in CCB order for entire database and second, it searches for frequent pattern in CCB order. This method is using concept of reduced support and it reduces the time complexity.
CITED BY (2)  
1 Ali, S. Z., & Rathore, Y. A comprehensive study of major techniques of multi level frequent pattern mining: a survey.
2 Herawan, T., Noraziah, A., Abdullah, Z., Deris, M. M., & Abawajy, J. H. (2012). EFP-M2: efficient model for mining frequent patterns in transactional database. In Computational Collective Intelligence. Technologies and Applications (pp. 29-38). Springer Berlin Heidelberg.
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Dr. K.Duraiswamy
- India
Associate Professor Jayanthi Balasubramaniam
Kongu Arts and Science College - India
sjaihere@gmail.com