Multidimensional Association Rules on Tensors

Ryohei Yokobayashi, T. Miura
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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 timeand spacecomplexities. Here we show TDM allows us to model several operations specific to multidimensional data mining yet to reduce amount of description.
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张量上的多维关联规则
本文提出了一种基于张量的多维数据挖掘框架。张量数据模型(TDM)为信息检索提供了高阶数据结构和朴素描述。其中,我们在这里讨论多维规则挖掘。通常,关联规则挖掘(或关联规则的提取)关注的是单个谓词的关联事务记录,由于时间和空间复杂性,很难检查多个谓词上的事务记录。这里我们展示了TDM允许我们对特定于多维数据挖掘的几个操作进行建模,同时减少了描述的数量。
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