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Association Rule Discovery in Data Mining by Implementing Principal Component Analysis

  • Bobby D. Gerardo
  • Jaewan Lee
  • Inho Ra
  • Sangyong Byun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3397)

Abstract

This paper presents the Principal Component Analysis (PCA) which is integrated in the proposed architectural model and the utilization of apriori algorithm for association rule discovery. The scope of this study includes techniques such as the use of devised data reduction technique and the deployment of association rule algorithm in data mining to efficiently process and generate association patterns. The evaluation shows that interesting association rules were generated based on the approximated data which was the result of dimensionality reduction, thus, implied rigorous and faster computation than the usual approach. This is attributed to the PCA method which reduces the dimensionality of the original data prior to the processing. Furthermore, the proposed model had verified the premise that it could handle sparse information and suitable for data of high dimensionality as compared to other technique such as the wavelet transform.

Keywords

Data Mining Association Rule Frequent Itemsets Data Cube Data Mining Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bobby D. Gerardo
    • 1
  • Jaewan Lee
    • 1
  • Inho Ra
    • 1
  • Sangyong Byun
    • 2
  1. 1.School of Electronic and Information EngineeringKunsan National UniversityChonbukSouth Korea
  2. 2.Faculty of Telecommunication & Computer EngineeringCheju National UniversityJeju-doSouth Korea

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