UNITED STATES
Mining Frequent Patterns with Counting Inference at Multiple Levels
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10.5120/778-1100 |
Deepika Sirohi, Ruchika Yadav and Mittar Vishav. Article: Mining Frequent Patterns with Counting Inference at Multiple Levels. International Journal of Computer Applications 3(10):1–6, July 2010. Published By Foundation of Computer Science. BibTeX
@article{key:article,
author = {Deepika Sirohi and Ruchika Yadav and Mittar Vishav},
title = {Article: Mining Frequent Patterns with Counting Inference at Multiple Levels},
journal = {International Journal of Computer Applications},
year = {2010},
volume = {3},
number = {10},
pages = {1--6},
month = {July},
note = {Published By Foundation of Computer Science}
}
Abstract
Mining association rules at multiple levels helps in finding more specific and relevant knowledge. While computing the number of frequency of an item we need to scan the given database many times. So we used counting inference approach for finding frequent itemsets at each concept levels which reduce the number of scan. In this paper, we purpose a new algorithm LWFT which follow the top-down progressive deepening method and it is based on existing algorithms for finding multiple level association rules. This algorithm is efficient for finding frequent itemsets from large databases.
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