Abstract
This paper handles the integration of fuzziness with On-Line Analytical Processing (OLAP) association rules mining. It contributes to the ongoing research on multidimensional online data mining by proposing a general architecture that uses a fuzzy data cube for knowledge discovery. Three different methods are introduced to mine fuzzy association rules in the constructed fuzzy data cube, namely single dimension, multidimensional and hybrid association rules mining; the third structure integrates the other two methods. To the best of our knowledge, this is the first effort in this direction. Experimental results obtained for each of the three methods on the adult data of the United States census in 2000 show the effectiveness and applicability of the proposed mining approach.
The research of this author is partially supported by NSERC grant and University of Calgary grant.
OLAP is one of the most popular tools for on-line, fast and effective multidimensional data analysis.
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Kaya, M., Alhajj, R. (2003). Integrating Fuzziness with OLAP Association Rules Mining. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_31
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DOI: https://doi.org/10.1007/3-540-45065-3_31
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