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Reseach Article
Efficient Mining of Frequent Itemsets using Improved FP-Growth Algorithm
| 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
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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