Dataset-Transformation: Improving Clustering by enhancing the structure with DipScaling and DipTransformation

Author(s)
Benjamin Schelling, Claudia Plant
Abstract

A data set might have a well-defined structure, but this does not necessarily lead to good clustering results. If the structure is hidden in an unfavourable scaling, clustering will usually fail. The aim of this work is to present techniques - DipScaling and DipTransformation - which enhance the data set by re-scaling and transforming its features and thus emphasizing and accentuating its structure. If the structure is sufficiently clear, clustering algorithms will perform far better. We refer to such techniques as "Dataset-Transformations" and try to provide a mathematical framework for them. To show that our algorithms work well, we have conducted extensive experiments on several real-world data sets, where we improve clustering not only for k-means, which is our main focus but also for other standard clustering approaches.

Organisation(s)
Research Group Data Mining, Research Platform Data Science @ Uni Vienna
Journal
Knowledge and Information Systems
ISSN
0219-1377
Publication date
08-2019
Publication status
Published
Peer reviewed
Yes
Austrian Fields of Science 2012
102033 Data mining
Portal url
https://ucris.univie.ac.at/portal/en/publications/datasettransformation-improving-clustering-by-enhancing-the-structure-with-dipscaling-and-diptransformation(a1ac9065-a4a6-4e94-9ea2-0384b0224fdc).html