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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Unsupervised Change Analysis Using Supervised Learning
verfasst von : Shohei Hido, Tsuyoshi Idé, Hisashi Kashima, Harunobu Kubo, Hirofumi Matsuzawa
Erschienen in: Advances in Knowledge Discovery and Data Mining
Verlag: Springer Berlin Heidelberg
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We propose a formulation of a new problem, which we call
change analysis
, and a novel method for solving the problem. In contrast to the existing methods of change (or outlier) detection, the goal of change analysis goes beyond detecting whether or not any changes exist. Its ultimate goal is to find the explanation of the changes. While change analysis falls in the category of unsupervised learning in nature, we propose a novel approach based on
supervised
learning to achieve the goal. The key idea is to use a supervised classifier for interpreting the changes. A classifier should be able to discriminate between the two data sets if they actually come from two different data sources. In other words, we use a hypothetical label to train the supervised learner, and exploit the learner for interpreting the change. Experimental results using real data show the proposed approach is promising in change analysis as well as concept drift analysis.