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.
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Gerardo, B.D., Lee, J., Ra, I., Byun, S. (2005). Association Rule Discovery in Data Mining by Implementing Principal Component Analysis. In: Kim, T.G. (eds) Artificial Intelligence and Simulation. AIS 2004. Lecture Notes in Computer Science(), vol 3397. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30583-5_6
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DOI: https://doi.org/10.1007/978-3-540-30583-5_6
Publisher Name: Springer, Berlin, Heidelberg
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