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.
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: 22 Feb 2019 *