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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References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of International Conference on Very Large Databases VLDB, pp. 487–499 (1994)
Han, J., Kamber, M.: Data mining concepts & techniques. Morgan Kaufmann, USA (2001)
Hellerstein, J.L., Ma, S., Perng, C.S.: Discovering actionable patterns in event data. IBM Systems Journal 41(3) (2002)
Multi-Dimensional Constrained Gradient Mining, ftp://fas.sfu.ca/pub/cs/theses/2001/JoyceManWingLamMSc.pdf
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of ACM SIGMOD International Conference on Mngt. of Data (1993)
Chen, B., Haas, P., Scheuermann, P.: A new two-phase sampling based algorithm for discovering association rules. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2002)
Bronnimann, H., Chen, B., Dash, M., Hass, P., Qiao, Y., Scheuermann, P.: Efficient Data-Reduction Methods for On-Line Association Rule Discovery. In: Data Mining: Next Generation Challenges & Future Directions (2004) (in press)
Margaritis, D., Faloutsos, C., Thrun, S.: NetCube: A Scalable Tool for Fast Data Mining and Compression. In: 27th Conference on Very Large Databases (VLDB) Roma, Italy (September 2001)
Korn, F., Labrinidis, A., Kotidis, Y., Faloutsos, C., Kaplunovich, A., Perkovic, D.: Quantifiable Data Mining Using Principal Component Analysis Technical Report, University of Maryland, College Park, Number CS-TR-3754 (February 1997)
Han, E.H., Karypis, G., Kumar, V., Mobasher, B.: Clustering in a high-dimensional space using hypergraph models (1998), Available at, http://www.informatik.uni-siegen.de/~galeas/papers/general/Clustering_in_a_High-Dimensional_Space_Using_Hypergraphs_Models_%28Han1997b%29.pdf
Gerardo, B., Lee, J.W., Lee, J.S., Park, M.G., Lee, M.R.: The association rule algorithm with missing data in data mining. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3043, pp. 97–105. Springer, Heidelberg (2004)
Principal Component Analysis, http://www.unesco.org/webworld/idams/advguide/Chapt6_2.htm
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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
Print ISBN: 978-3-540-24476-9
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