Association Rule Mining for Continuous Attributes using Genetic Network Programming

Abstract

Most of the existing association rule mining algorithms are able to extract knowledge from databases with attributes of binary values. However, in real-world applications, databases are usually composed of continuous values such as height, length or weight. If the attributes are continuous, the algorithms are commonly integrated with a discretization method that transforms them into discrete attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval into a discrete numerical value. However, the user most often must specify the number of intervals, or provide some heuristic rules to be used while discretization, and then it is difficult to get the highest attribute interdependency and at the same time get the lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical databases. We propose a method using a new graph-based evolutionary algorithm named 'genetic network programming (GNP)' that can deal with continuous values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolves them in order to find a solution; this feature contributes to creating very compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rules is measured by the use of χ 2 test, and only important association rules are stored in a pool all together through generations. Results of experiments conducted on a real-life database suggest that the proposed method provides an effective technique for handling continuous attributes. 

Functional Association Rule Mining Using Cooperative Coevolutionary Deep Neural Networks

“…Genetic programming for mining continuous variable is used in [21]. Fuzzy sets are also used as a tool to overcome the problems brought by discretization [22], [23].…”

Robust intelligent PCA‐based face recognition framework using GNP‐fuzzy data mining

“…The judgment node has a one‐to‐one correspondence with the attribute in the database. The transition in the judgment nodes represents a subset of candidate CARs [20], while the processing node calculates the importance measures of those candidate CARs. As shown in Fig.…”

“…Using the geometric distances in the traditional PCA‐based face recognition algorithm is another problem leading to the low accuracy and low robustness under the MTIP‐CID condition. In this section, a robust FCAR‐based classifier constructed by GNP‐FDM [20] is applied to all the clusters of \documentclass{article}\usepackage{amsmath}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{amsfonts}\pagestyle{empty}\begin{document}$\mathrm{{FD}}_{\mathrm{{pca}}}^{2}$\end{document} to solve this problem. Compared with traditional CARs‐based classifiers, fuzzification is used in the GNP‐FDM to deal with the continuous attributes in \documentclass{article}\usepackage{amsmath}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{amsfonts}\pagestyle{empty}\begin{document}$\mathrm{{FD}}_{\mathrm{{pca}}}^{2}$\end{document} and to reduce the influence of the fluctuations of the instances caused by noise on the recognition accuracy.…”

“…And most of them are pruned when building a CARs‐based classifier. As a result, EA‐based CARs mining methods are proposed to achieve the balance between the number and quality of CARs represented by GNP [20], GA [18], and GP [19]. As explained in Section 2.1, GNP‐based data mining has a better performance compared with GA‐ and GP‐based methods.…”

Galois Closure Based Association Rule Mining From Biological Data

“…Genetic Programming This theory is a biologically inspired evolutionary theory for solving multiobjective optimization problems. Recent developments in association rule mining using genetic network programming are described in [158,167,186]. Genetic programming was used for mining association rules in temporal data [79] and an extension called Grammar Guided Genetic Programming (G3P ) for solving the problem of generating invalid individuals was proposed [47,179].…”