Title: EFP-tree: an efficient FP-tree for incremental mining of frequent patterns

Authors: Razieh Davashi; Mohammad-Hossein Nadimi-Shahraki

Addresses: Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran ' Faculty of Computer Engineering Najafabad Branch, Islamic Azad University, Najafabad, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Abstract: Frequent pattern mining from dynamic databases where there are many incremental updates is a significant research issue in data mining. After incremental updates, the validity of the frequent patterns is changed. A simple way to handle this state is rerunning mining algorithms from scratch which is very costly. To solve this problem, researchers have introduced incremental mining approach. In this article, an efficient FP-tree named EFP-tree is proposed for incremental mining of frequent patterns. For original database, it is constructed like FP-tree by using an auxiliary list without any reconstruction. Consistently, for incremental updates, EFP-tree is reconstructed once and therefore reduces the number of tree reconstructions, reconstructed branches and the search space. The experimental results show that using EFP-tree can reduce reconstructed branches and the runtime in both static and incremental mining and enhance the scalability compared to well-known tree structures CanTree, CP-tree, SPO-tree and GM-tree in both dense and sparse datasets.

Keywords: data mining; dynamic databases; frequent pattern; incremental mining; FP-tree.

DOI: 10.1504/IJDMMM.2019.098958

International Journal of Data Mining, Modelling and Management, 2019 Vol.11 No.2, pp.144 - 166

Received: 26 Nov 2016
Accepted: 02 Sep 2017

Published online: 10 Apr 2019 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article