Abstract
The problem of association rule mining has gained considerable prominence in the data mining community for its use as an important tool of knowledge discovery from large-scale databases. And there has been a spurt of research activities around this problem. Traditional association rule mining is limited to intra-transaction. Only recently the concept ofN-dimensional inter-transaction association rule (NDITAR) was proposed by H.J. Lu. This paper modifies and extends Lu’s definition of NDITAR based on the analysis of its limitations, and the generalized multidimensional association rule (GMDAR) is subsequently introduced, which is more general, flexible and reasonable than NDITAR.
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This work was supported by the National Natural Science Foundation of China, the NKBRSF of China, and the National Doctoral Subject Foundation of China.
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Zhou, A., Zhou, S., Jin, W. et al. Generalized multidimensional association rules. J. Comput. Sci. & Technol. 15, 388–392 (2000). https://doi.org/10.1007/BF02948876
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DOI: https://doi.org/10.1007/BF02948876