A Clustering-Based Approach to the Mining of Analogical Proportions

被引:7
|
作者
Beltran, William Correa [1 ]
Jaudoin, Helene [1 ]
Pivert, Olivier [1 ]
机构
[1] Univ Rennes 1, IRISA Shaman, Lannion, France
来源
2015 IEEE 27TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2015) | 2015年
关键词
analogy; parallels; database; ALGORITHMS;
D O I
10.1109/ICTAI.2015.31
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an approach aimed at mining a new type of pattern in data, namely analogical proportions. An analogical proportion expresses the equality of the relationships between the attributes of two pairs of structured objects. This notion is investigated in the database context for the discovery of different forms of "parallels" between pairs of tuples. First, we give a formal definition of the analogical proportion in the setting of relational databases. Then we focus on the problem of mining analogical proportions. We propose to use a clustering approach for enumerating parallels occurring in a relation, thus discovering analogical proportions.
引用
收藏
页码:125 / 131
页数:7
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