@article {10.3844/ajebasp.2011.58.65,
article_type = {journal},
title = {Using Unique-Prime-Factorization Theorem to Mine Frequent Patterns without Generating Tree},
author = {Tohidi, Hossein and Ibrahim, Hamidah},
volume = {3},
year = {2011},
month = {Jan},
pages = {58-65},
doi = {10.3844/ajebasp.2011.58.65},
url = {https://thescipub.com/abstract/ajebasp.2011.58.65},
abstract = {Problem statement: 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. Approach: 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. Results: 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). Conclusion/Recommendations: This algorithm is able to achieve low memory order at O(1) which is significantly better than FP-growth.},
journal = {American Journal of Economics and Business Administration},
publisher = {Science Publications}
}