Mark Hoogendoorn*, Burkhardt Funk

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review


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.

Original languageEnglish
Title of host publicationMachine Learning for the Quantified Self
Subtitle of host publicationOn the Art of Learning from Sensory Data
Number of pages28
ISBN (Electronic)9783319663081
ISBN (Print)9783319663074
Publication statusPublished - 1 Jan 2018

Publication series

NameCognitive Systems Monographs
ISSN (Print)1867-4925
ISSN (Electronic)1867-4933


Dive into the research topics of 'Clustering'. Together they form a unique fingerprint.

Cite this