Associative prediction model and clustering for product forecast data

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

3 Citations (Scopus)

Abstract

Association rules are adopted to discover the interesting relationship and knowledge in a large dataset. Knowledge may appear in terms of a frequent pattern discovered in a large number of production data. This knowledge can improve or solve production problems to achieve low cost production. To obtain knowledge and quality information, data mining can be applied to the manufacturing industry. In this study, we used one of the association rule approach, i.e. Apriori algorithm to build an associative prediction model for product forecast data. Also, we adopt the simplest method in clustering, k-means algorithm to attain the link between patterns. The real industrial product forecast data for one year duration is used in the experiment. This data consists of 42 products with two important attributes, i.e. time in the week and required quantity. Since the data mining processes need a large amount of data, we simulated these data by using the Monte Carlo technique to obtain another 15 years of simulated forecast data. There are two main experiments for the association rules mining and clustering. As a result, we obtain an associative prediction model and clustering for the forecasting data. The extracted model provides the prediction knowledge about the range of production in a certain period.

Original languageEnglish
Title of host publicationProceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
Pages1459-1464
Number of pages6
DOIs
Publication statusPublished - 2010
Event2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 - Cairo
Duration: 29 Nov 20101 Dec 2010

Other

Other2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
CityCairo
Period29/11/101/12/10

Fingerprint

Association rules
Data mining
Experiments
Costs
Industry

Keywords

  • Association rules
  • Associative
  • Clustering
  • Manufacturing
  • Prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture

Cite this

Ismail, R., Othman, Z., & Abu Bakar, A. (2010). Associative prediction model and clustering for product forecast data. In Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 (pp. 1459-1464). [5687116] https://doi.org/10.1109/ISDA.2010.5687116

Associative prediction model and clustering for product forecast data. / Ismail, Ruhaizan; Othman, Zalinda; Abu Bakar, Azuraliza.

Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. p. 1459-1464 5687116.

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

Ismail, R, Othman, Z & Abu Bakar, A 2010, Associative prediction model and clustering for product forecast data. in Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10., 5687116, pp. 1459-1464, 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10, Cairo, 29/11/10. https://doi.org/10.1109/ISDA.2010.5687116
Ismail R, Othman Z, Abu Bakar A. Associative prediction model and clustering for product forecast data. In Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. p. 1459-1464. 5687116 https://doi.org/10.1109/ISDA.2010.5687116
Ismail, Ruhaizan ; Othman, Zalinda ; Abu Bakar, Azuraliza. / Associative prediction model and clustering for product forecast data. Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. pp. 1459-1464
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