文章基本信息

标题：Mining Classification Rules from Multidimensional Structured Databases
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作者：Tomonobu Ozaki ; Tomoki Watanuma ; Takenao Ohkawa 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2007
卷号：22
期号：2
页码：173-182
DOI：10.1527/tjsai.22.173
出版社：The Japanese Society for Artificial Intelligence
摘要：Recently, the research area of mining in structured data has been actively studied. However, since most techniques for structured data mining so far specialize in mining from single structured data, it is difficult for these techniques to handle more realistic data which is related to various types of attribute and which consists of plural kinds of structured data. Since such kind of data is expected to be going to rapidly increase, we need to establish a flexible and highly accurate technique that can inclusively treat such kind of data. In this paper, as one of the techniques to deal with such kind of data, we propose data mining algorithms of mining classification rules in multidimensional structured data. First, an algorithm with two pruning capabilities of mining correlated patterns is introduced. Then, top- k multidimensional correlated patterns are discovered by using this algorithm repeatedly in the fashion like a beam search. We also show the algorithms for constructing classifiers based on the discovered patterns. Experiments with real world data were conducted to assess the effectiveness of the proposed algorithms. The results show that the proposed algorithms can construct comprehensible and accurate classifiers within a reasonable running time.
关键词：structured data mining ; classification ; top-k patterns ; correlated patterns