文章基本信息

标题：Conceptual Clustering Algorithms Using Regression Analysis
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作者：Hiroshi Tsukimoto ; Makoto Sato
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：2001
卷号：16
期号：3
页码：344-352
DOI：10.1527/tjsai.16.344
出版社：The Japanese Society for Artificial Intelligence
摘要：This paper presents conceptual clustering algorithms using regression analysis. The basic idea is that given data can be classified to the class “existing” and so conceptual clustering(unsupervised learning) is transformed to classification (supervised learning). The algorithms consist of transforming given data to the data with a class, obtaining a function({0, 1}ⁿ →[0, 1]) by regression analysis, approximating the function by a Boolean function, and generating a concept hierarchy from the Boolean function. Regression analysis includes linear regression analysis and nonlinear regression analysis by neural networks. The algorithms can perform the multiple classification and generate simple clusters. The algorithms using linear regression analysis and neural networks have been applied to real data. Results show that the algorithm using neural networks works well.
关键词：conceptual clustering ; unsupervised learning ; regression analysis ; neural networks