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Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
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Efficient Mining of Frequent Itemsets using Improved FP-Growth Algorithm

by Abdulkader M. Al-Badani, Basheer M. Al-Maqaleh
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 14
Year of Publication: 2018
Authors: Abdulkader M. Al-Badani, Basheer M. Al-Maqaleh
10.5120/ijais2018451766

Abdulkader M. Al-Badani, Basheer M. Al-Maqaleh . Efficient Mining of Frequent Itemsets using Improved FP-Growth Algorithm. International Journal of Applied Information Systems. 12, 14 ( July 2018), 15-20. DOI=10.5120/ijais2018451766

@article{ 10.5120/ijais2018451766,
author = { Abdulkader M. Al-Badani, 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 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number14/1036-2018451766/ },
doi = { 10.5120/ijais2018451766 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:09:20.495668+05:30
%A Abdulkader M. Al-Badani
%A Basheer M. Al-Maqaleh
%T Efficient Mining of Frequent Itemsets using Improved FP-Growth Algorithm
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 14
%P 15-20
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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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Index Terms

Computer Science
Information Sciences

Keywords

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

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