Association rules mining for handling continuous attributes using genetic network programming and fuzzy membership functions

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

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

1 Citation (Scopus)

Abstract

Data Mining is the process of extracting useful hidden knowledge from large volumes of data and its results can be used in decision support systems. Several data mining algorithms have been developed; one example is the association rule mining, which discovers associations among items encoded within a database. When the values of the attributes in a database are continuous such as height, length or weight, their domain is usually discretized into several intervals, as a result, such attributes are handled as discrete attributes. In this study, a new approach of mining association rules for handling continuous attributes that does not use any discretization is proposed. The methodology is based on a new graph-based evolutionary algorithm named "Genetic Network Programming (GNP)" and fuzzy membership functions. Our data mining method first needs to transform the continuous values in transactions into linguistic terms, then judge them and find association rules using GNP. GNP represent its individuals using graph structures that evolve in order to find a solution; this feature contributes to creating quite compact programs and implicitly memorizing past action sequences. The proposed method can measure the significance of the extracted association rules by using support, confidence and χ2 test, and obtains a sufficient number of important association rules in a short time.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages2723-2729
Number of pages7
DOIs
Publication statusPublished - 2007
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu
Duration: 2007 Sep 172007 Sep 20

Other

OtherSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CityTakamatsu
Period07/9/1707/9/20

Fingerprint

Association rules
Membership functions
Data mining
Decision support systems
Linguistics
Evolutionary algorithms

Keywords

  • Association rules
  • Data mining
  • Fuzzy membership functions
  • Genetic network programming

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Taboada, K., Shimada, K., Mabu, S., Hirasawa, K., & Furuzuki, T. (2007). Association rules mining for handling continuous attributes using genetic network programming and fuzzy membership functions. In Proceedings of the SICE Annual Conference (pp. 2723-2729). [4421451] https://doi.org/10.1109/SICE.2007.4421451

Association rules mining for handling continuous attributes using genetic network programming and fuzzy membership functions. / Taboada, Karla; Shimada, Kaoru; Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki.

Proceedings of the SICE Annual Conference. 2007. p. 2723-2729 4421451.

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

Taboada, K, Shimada, K, Mabu, S, Hirasawa, K & Furuzuki, T 2007, Association rules mining for handling continuous attributes using genetic network programming and fuzzy membership functions. in Proceedings of the SICE Annual Conference., 4421451, pp. 2723-2729, SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007, Takamatsu, 07/9/17. https://doi.org/10.1109/SICE.2007.4421451
Taboada K, Shimada K, Mabu S, Hirasawa K, Furuzuki T. Association rules mining for handling continuous attributes using genetic network programming and fuzzy membership functions. In Proceedings of the SICE Annual Conference. 2007. p. 2723-2729. 4421451 https://doi.org/10.1109/SICE.2007.4421451
Taboada, Karla ; Shimada, Kaoru ; Mabu, Shingo ; Hirasawa, Kotaro ; Furuzuki, Takayuki. / Association rules mining for handling continuous attributes using genetic network programming and fuzzy membership functions. Proceedings of the SICE Annual Conference. 2007. pp. 2723-2729
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