Template-Type: ReDIF-Article 1.0
Author-Name: Hossein Tohidi
Author-Name: Hamidah Ibrahim
Title:  Using Unique-Prime-Factorization Theorem to Mine Frequent Patterns without Generating Tree
Abstract:  <strong>Problem statement: </strong>Ffrequent patterns are patterns that appear in a data set frequently. Finding such frequent patterns plays an essential role in mining associations, correlations and many other interesting relationships among data. <strong>Approach:</strong> Most of the previous studies adopt an Apriorilike approach. For huge database it may need to generate a huge number of candidate sets. An interest solution is to design an approach that without generating candidate is able to mine frequent patterns. <strong>Results: </strong>An interesting method to frequent pattern mining without generating candidate pattern is called frequent-pattern growth, or simply FP-growth, which adopts a divide-and-conquer strategy as follows. However, for a large database, constructing a large tree in the memory is a time consuming task and increase the time of execution. In this study we introduce an algorithm to generate frequent patterns without generating a tree and therefore improve the time complexity and memory complexity as well. Our algorithm works based on prime factorization and is called Prime Factor Miner (PFM). <strong>Conclusion/Recommendations:</strong> This algorithm is able to achieve low memory order at O(1) which is significantly better than FP-growth.
Keywords: Data mining, frequent pattern mining, association rule mining
Journal: American Journal of Economics and Business Administration
Pages: 58-65
Volume: 3
Issue: 1
Year: 2011
Month: January
DOI: 10.3844/ajebasp.2011.58.65
File-URL: https://thescipub.com/pdf/ajebasp.2011.58.65.pdf
File-Format: Application/pdf
File-URL: https://thescipub.com/abstract/ajebasp.2011.58.65
File-Format: text/html
Handle: RePEc:abk:jajeba:ajebasp.2011.58.65