Abstract
We introduce a distance measure based on the idea that two vectors are considered similar if they lead to similar predictive probability distributions. The suggested approach avoids the scaling problem inherent to many alternative techniques as the method automatically transforms the original attribute space to a probability space where all the numbers lie between 0 and 1. The method is also flexible in the sense that it allows different attribute types (discrete or continuous) in the same consistent framework. To study the validity of the suggested measure, we ran a series of experiments with publicly available data sets. The empirical results demonstrate that the unsupervised distance measure is sensible in the sense that it can be used for discovering the hidden clustering structure of the data.
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Kontkanen, P., Lahtinen, J., Myllymäki, P., Tirri, H. (2000). An Unsupervised Bayesian Distance Measure. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_14
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DOI: https://doi.org/10.1007/3-540-44527-7_14
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