The lattice‐based approaches for mining association rules: a review

Abstract

The traditional methods for mining association rules (ARs) include two phrases: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) and generating ARs from FIs/FCIs/FMIs. Lattice‐based approaches (LBAs) for mining ARs are new approaches including two phrases: frequent itemset lattice (FIL)/frequent closed itemset lattice (FCIL) building and generating ARs from the lattice. Total mining time of LBAs for mining ARs outperforms the traditional methods for mining ARs. Besides, the most important advantage of LBAs for mining ARs is that the algorithms only build the lattice once and mine ARs with many different confidences or many different minimum supports (the thresholds have to be greater than or equal to the threshold used to build lattices) without mining FIs/FCIs again. In this article, we describe a number of existing LBAs for mining ARs on static databases including lattice building and rule generation. In addition, in today's online system, the data often change in several operations such as insert, delete, and update. Hence, a number of LBAs for mining ARs on dynamic databases are mentioned. Finally, complexity analysis of the LBAs for mining ARs is also thoroughly discussed. WIREs Data Mining Knowl Discov 2016, 6:140–151. doi: 10.1002/widm.1181 This article is categorized under: Algorithmic Development > Association Rules

Analysis of green manure decomposition parameters in northeast Brazil using association rule networks

“…Data mining techniques, particularly mining with association rules, may help to identify the parameters related to agroecological production (de Barros et al, 2013). The identification of association rules is a data mining technique that aims to identify specific data patterns in large databases, which can be interpreted to obtain specific knowledge regarding the problem under consideration (Le and Vo, 2016).…”

“…Association rules are used as data mining techniques to identify specific patterns in the data found in large databases, where the interpretation of these patterns can allow the extraction of specific knowledge about the problem under consideration (Le and Vo, 2016;Kuo et al, 2014).…”

Nuclear: An Efficient Method for Mining Frequent Itemsets Based on Kernels and Extendable Sets

“…In [20], Tran et al proposed the GEN_ITEMSETS algorithm to generate all itemsets from a lattice of FCIs and generators without repetitions. More recently, Le and Vo [12] proposed an N-list-based algorithm for mining FCIs, named NAFCP. The experimental evaluation of this work has shown that NAFCP outperforms state-of-art FCI mining algorithms in terms of runtime and memory usage in most cases.…”

“…The authors of Charm proposed the CharmL algorithm [8], which builds the lattice of FCIs. Formal concept (i.e., lattice of FCIs) analysis is also another way of mining FIs as well as ARs [6,12,13].…”

An Efficient Method for Mining Erasable Itemsets Using Multicore Processor Platform

“…It has attracted a lot of research and become an ideal topic in recent years. There are many approaches, which were summarized in [13], to mine such patterns including META [12], MERIT [14], MEI [15], and EIFDD [16]. Several related problems of mining erasable closed itemsets [17], top-rank-k erasable itemsets [18], erasable itemsets with constraints [19], and weighted erasable itemsets [20,21] have also been developed.…”