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Titel:

DDCAL: Evenly Distributing Data into Low Variance Clusters Based on Iterative Feature Scaling

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Lux, Marian; Rinderle-Ma, Stefanie
Abstract:
This work studies the problem of clustering one-dimensional data points such that they are evenly distributed over a given number of low variance clusters. One application is the visualization of data on choropleth maps or on business process models, but without over-emphasizing outliers. This enables the detection and differentiation of smaller clusters. The problem is tackled based on a heuristic algorithm called DDCAL (1d distribution cluster algorithm) that is based on iterative feature scal...     »
Zeitschriftentitel:
Journal of Classification
Jahr:
2023
Monat:
January
Sprache:
en
Volltext / DOI:
doi:10.1007/s00357-022-09428-6
WWW:
https://doi.org/10.1007/s00357-022-09428-6
Print-ISSN:
1432-1343
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