Mining association rules from databases with continuous attributes using genetic network programming

Karla Taboada, Eloy Gonzales, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most association rule mining algorithms make use of discretization algorithms for handling continuous attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval to a discrete numerical value. However, by means of methods of discretization, it is difficult to get highest attribute interdependency and at the same time to get 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 continues values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolve them in order to find a solution; this feature contributes to creating quite compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rule is measured by the use of the chi-squared 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.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages1311-1317
Number of pages7
DOIs
Publication statusPublished - 2007
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 -
Duration: 2007 Sep 252007 Sep 28

Other

Other2007 IEEE Congress on Evolutionary Computation, CEC 2007
Period07/9/2507/9/28

Fingerprint

Network Programming
Genetic Network
Association Rule Mining
Association rules
Genetic Programming
Attribute
Discretization
Association Rules
Interval
Chi-squared test
Evolutionary algorithms
Interdependencies
Discretization Method
Graph in graph theory
Preprocessing
Evolutionary Algorithms
Lowest
Continue
Experiments
Experiment

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Cite this

Taboada, K., Gonzales, E., Shimada, K., Mabu, S., Hirasawa, K., & Furuzuki, T. (2007). Mining association rules from databases with continuous attributes using genetic network programming. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 1311-1317). [4424622] https://doi.org/10.1109/CEC.2007.4424622

Mining association rules from databases with continuous attributes using genetic network programming. / Taboada, Karla; Gonzales, Eloy; Shimada, Kaoru; Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki.

2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 1311-1317 4424622.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Taboada, K, Gonzales, E, Shimada, K, Mabu, S, Hirasawa, K & Furuzuki, T 2007, Mining association rules from databases with continuous attributes using genetic network programming. in 2007 IEEE Congress on Evolutionary Computation, CEC 2007., 4424622, pp. 1311-1317, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 07/9/25. https://doi.org/10.1109/CEC.2007.4424622
Taboada K, Gonzales E, Shimada K, Mabu S, Hirasawa K, Furuzuki T. Mining association rules from databases with continuous attributes using genetic network programming. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 1311-1317. 4424622 https://doi.org/10.1109/CEC.2007.4424622
Taboada, Karla ; Gonzales, Eloy ; Shimada, Kaoru ; Mabu, Shingo ; Hirasawa, Kotaro ; Furuzuki, Takayuki. / Mining association rules from databases with continuous attributes using genetic network programming. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. pp. 1311-1317
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