Abstract
This chapter focuses on clustering of the data resulting from quantified selves. It introduces distance functions that can be used to compare individual data points, but also entire datasets of users. Among these are dynamic time warping and the cross-correlation coefficient. The chapter provides a brief discussion of popular clustering techniques. In addition, it explains more specialized clustering techniques that are better suited for the quantified self, including subspace clustering and data stream mining.
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Hoogendoorn, M., Funk, B. (2018). Clustering. In: Machine Learning for the Quantified Self. Cognitive Systems Monographs, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-319-66308-1_5
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DOI: https://doi.org/10.1007/978-3-319-66308-1_5
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-66308-1
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