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Comparing Apples and Oranges - Measuring Differences between Data Mining Results

Comparing Apples and Oranges - Measuring Differences between Data Mining Results

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. Deciding whether the results of two different mining algorithms provide significantly different information is an important open problem in exploratory data mining. Whether the goal is to select the most informative result for analysis, or decide which mining approach will likely provide the most novel insight, it is essential that we can tell how different the information is that two results provide. In this paper we take a first step towards comparing exploratory results on binary data. We propose to meaningfully convert results into sets of noisy tiles, and compare between these sets byMaximum Entropy modelling and Kullback-Leibler... Show More

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