Modified binary cuckoo search for association rule mining

Abstract

This paper proposes a modified single-objective binary cuckoo search for association rule mining (MBCS-ARM). The proposed algorithm includes a novel representations of individuals, which tackles the problems of large dimensionality with an increasing number of attributes. The MBCS-ARM also supports the mining of rules, where intervals of attributes can either be negative or positive. It uses an objective function composed of support and confidence weighted by two parameters, which control the importance of each measure in the found rules. It is tested on eight publicly available databases, while also compared to several single-objective evolutionary algorithms, and traditional algorithms, all found in the KEEL software tool. The experiments show promising results of the MBCS-ARM, compared to other algorithms, by producing rules, which are interesting, simple, and also easy to understand, which is of great importance in domains like medicine.

Impact of Solution Representation in Nature-Inspired Algorithms for Feature Selection

“…According to the problem for which the algorithm was designed, the solution representation is one the more important factors for the success of finding an optimal solution. For some problems, like numerical function optimization [23] or constrained engineering optimization problems [26], solution coding and representation is an easy task, while some reflection is needed for problems like association rule mining [27]. In the first case, the best real-coded vector is searched for, while in the second, the solution is encoded into representation, and some kind of mapping the representation to a real-world solution is needed.…”

ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization

“…Song et al (2016) proposed a multi-objective binary at algorithm (MBBA) based on Pareto for association rule mining. Mlakar et al (2017) presented a single-objective binary cuckoo search using a novel individual representation. Other works can be found in (Pears & Koh, 2011;Song & Lee, 2017;Anuradha & Kumar, 2017;Feng et al, 2016;Huang et al, 2017;Vo, Pham, Le & Deng, 2017;Kieu, Vo, Le, Deng & Le, 2017).…”

“…In many cases, ARM generates extremely large number of association rules, which are impossible for end users to comprehend or validate, thereby limiting the usefulness of data mining results (Mlakar, Zorman, Fister & Fister, 2017). Numerous algorithms have been proposed to reduce the number of association rules (Chen, Shen, Chen, Shang & Wang, 2011;Shirsath & Verma, 2013;Mlakar, Zorman, Fister & Fister, 2017), such as generating only interesting rules or non-redundant rules, or rules satisfying certain criteria such as coverage, leverage, lift or strength (Yan, Sun & Liu, 2016). One of the most effective strategies for this problem is integrating optimization techniques with association rule mining for increasing its performance.…”

“…One of the most effective strategies for this problem is integrating optimization techniques with association rule mining for increasing its performance. Among the proposed algorithms are ARM-PSO (Kuo, Chao & Chiu, 2011), GA (Oladele & Sadiku, 2013), Cuckoo Search (Yun, Kim, Ryang, Lee & Lee, 2016;Mlakar et al, 2017), WFIM (Wensheng et al, 2017), HUIM-MMU (Jerry et al, 2016), CP-Miner (Thanh-Long, Bay & Vaclav, 2017), dynamic superset bit-vector (Tahrima et al, 2017) and lattice (Thang, Bay & Loan, 2017). However, most of the relevant algorithms for controlling large number of association rules are often computationally expensive and possibly generate much irrelevant rules.…”

Data Mining Big Data Inpatient Database Using Cuckoo Search

“…The goal of data mining is to extract useful information from a dataset and transform it into an understandable structure, which may be used directly or processed further by another algorithm. There are several methods for which are used for data mining, such as cluster analysis [4], dimensionality reduction [8], association rule mining [9], etc. Association rule mining has gained a lot of attention for mining interesting patterns from large databases within the research community.…”