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Efficient Mining of Frequent Itemsets using Improved FP-Growth Algorithm

Abdulkader M. Al-Badani, Basheer M. Al-Maqaleh in Algorithms

International Journal of Applied Information Systems
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Abdulkader M. Al-Badani, Basheer M. Al-Maqaleh
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  1. Abdulkader M Al-Badani and Basheer M Al-Maqaleh. Efficient Mining of Frequent Itemsets using Improved FP-Growth Algorithm. International Journal of Applied Information Systems 12(14):15-20, July 2018. URL, DOI BibTeX

    	author = "Abdulkader M. Al-Badani and Basheer M. Al-Maqaleh",
    	title = "Efficient Mining of Frequent Itemsets using Improved FP-Growth Algorithm",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "July 2018",
    	volume = 12,
    	number = 14,
    	month = "July",
    	year = 2018,
    	issn = "2249-0868",
    	pages = "15-20",
    	url = "",
    	doi = "10.5120/ijais2018451766",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


Frequent itemsets are itemsets that appear frequently in a dataset. Finding frequent itemsets plays an important role in association rules mining, correlations, and many other interesting relationships among data. Frequent itemset mining has been an active research area and a large number of algorithms have been developed. FP- Growth algorithm is currently one of the best approaches to frequent itemsets mining. It constructs a tree structure from transaction dataset and recursively traverse this tree to extract frequent itemsets in a depth first search manner. Also, it takes time to build an FP-tree, suffers from the increasing size of FP-tree and generating large number of frequent itemsets. In this paper, an improved frequent itemsets mining algorithm based on FP-Growth algorithm is proposed. The proposed algorithm uses a two dimensional array structure called Ordered Frequent Itemsets Matrix (OFIM) to construct a highly compact FP-tree. It greatly circumvents repeated scanning of datasets and it reduces the computational time, and reduces the number of frequent items that are generated obtaining significantly improved performance for FP-tree based algorithms.


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FP-Growth Algorithm, Aprioiri Algorithm, FP-tree, Support Count, Ordered Frequent Itemset Matrix