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arXiv · 1601.02213

On Clustering Time Series Using Euclidean Distance and Pearson Correlation

Michael R. Berthold·Frank Höppner
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Abstract

For time series comparisons, it has often been observed that z-score normalized Euclidean distances far outperform the unnormalized variant. In this paper we show that a z-score normalized, squared Euclidean Distance is, in fact, equal to a distance based on Pearson Correlation. This has profound impact on many distance-based classification or clustering methods. In addition to this theoretically sound result we also show that the often used k-Means algorithm formally needs a mod ification to keep the interpretation as Pearson correlation strictly valid. Experimental results demonstrate that in many cases the standard k-Means algorithm generally produces the same results.

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Michael R. Berthold, Frank Höppner. 2016-01-10. On Clustering Time Series Using Euclidean Distance and Pearson Correlation. https://arxiv.org/abs/1601.02213

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