On Agglomerative Hierarchical Percentile Clustering

DOI

10.2991/asum.k.210827.083How to use a DOI?

Keywords

Cluster analysis, distance distribution function, quantiles

Abstract

Cluster analysis aims at grouping objects represented by some feature vectors and as such revealing insight into subset structures among the considered objects. However, in many cases, the observations are subject to experimental errors and/or uncertainty. In such a case, a popular way is to summarize first the information about each object and, then, aggregate the objects via some cluster algorithm. The percentile clustering by Janowitz and Schweizer, instead, considers the whole distribution of observed features and, only afterwards, aggregates them. Here, we revisit this approach in an agglomerative clustering perspective. Moreover, we perform a simulation study showing some finite sample performance of the algorithm. Some case studies illustrate the advantages of the whole methodology.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Fabrizio Durante
AU  - Aurora Gatto
AU  - Susanne Saminger-Platz
PY  - 2021
DA  - 2021/08/30
TI  - On Agglomerative Hierarchical Percentile Clustering
BT  - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
PB  - Atlantis Press
SP  - 616
EP  - 623
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.083
DO  - 10.2991/asum.k.210827.083
ID  - Durante2021
ER  -