TRAINING SAMPLE DIMENSION REDUCTION BASED ON ASSOCIATION RULES
Keywords:association rule, confidence, model, support, reduction, training sample, term.
AbstractThe problem of training sample reduction is considered. A method for data reduction based on association rules is developed. The proposed method of training sample dimensionality reduction includes stages of reduction of instances, features and redundant terms, to evaluate the informativety of features uses the information about the extracted association rules. The developed method allows to create a partition of the feature space with less examples than in the original sample, which in turn allows the synthesis of simpler and more convenient for the perception of the diagnostic model. The practical value of these results is that on the basis of the proposed method the practical problem of reducing the training sample for the synthesis of the diagnostic model of quality confectionery products is solved.
Chaudhuri, A. Survey sampling theory and methods / A. Chaudhuri, H. Stenger. – New York : Chapman & Hall, 2005. – 416 p.
Encyclopedia of survey research methods / ed. P. J. Lavrakas. – Thousand Oaks : Sage Publications, 2008. – Vol. 1–2. – 968 p.
Кокрен, У. Методы выборочного исследования / У. Кокрен ; пер. с англ. И. М. Сонина ; под ред. А. Г. Волкова, Н. К. Дружинина. – М. : Статистика, 1976. – 440 с.
Jensen R. Computational intelligence and feature selection: rough and fuzzy approaches / R. Jensen, Q. Shen. – Hoboken: John Wiley & Sons, 2008. – 339 p.
Интеллектуальные информационные технологии проектирования автоматизированных систем диагностирования и распознавания образов : монография / [С. А. Субботин, Ан. А. Олейник, Е. А. Гофман, С. А. Зайцев, Ал. А. Олейник ; под ред. С. А. Субботина]. – Харьков : ООО «Компания Смит», 2012. – 317 с.
Gkoulalas-Divanis, A. Association Rule Hiding for Data Mining / A. Gkoulalas-Divanis,V. S. Verykios. – New York : Springer-Verlag, 2010. – 150 p.
Koh, Y. S. Rare Association Rule Mining and Knowledge Discovery / Y. S. Koh, N. Rountree. – New York : Information Science Reference, 2009. – 320 p.
Zhang, C. Association rule mining: models and algorithms / C. Zhang, S. Zhang. – Berlin : Springer-Verlag. – 2002. – 238 p.
Zhao, Y. Post-mining of association rules: techniques for effective knowledge extraction / Y. Zhao, C. Zhang, L. Cao. – New York : Information Science Reference, 2009. – 372 p.
Encyclopedia of artificial intelligence / Eds.: J. R. Dopico, J. D. de la Calle, A. P. Sierra. – New York : Information Science Reference, 2009. – Vol. 1–3. – 1677 p.
How to Cite
Copyright (c) 2014 T. Zayko, A. Oliinyk, S. Subbotin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.