Multidimensional Association Rules on Tensors
Ryohei Yokobayashi, Takao Miura
International Journal of Computational Linguistics Research, Vol. 9, No. 3, 2018.
DOI: https://doi.org/10.6025/jcl/2018/9/3/106-119
Link: http://www.dline.info/jcl/fulltext/v9n3/jclv9n3_2.pdf
Abstract:
In this work, we propose a framework suitable for multidimensional data mining based on tensor. A Tensor Data
Model (TDM) provides us with high order data structure and naive description for information retrieval. Among others, we
discuss multidimensional rule mining here. Generally, association rule mining (or extraction of association rules) concerns
about co-related transaction records of single predicate, and hard to examine the ones over multiple predicates since it takes
heavy time- and space- complexities. Here we show TDM allows us to model several operations specific to multidimensional
data mining yet to reduce amount of description.