On Agglomerative Hierarchical Percentile Clustering
- 10.2991/asum.k.210827.083How to use a DOI?
- Cluster analysis, distance distribution function, quantiles
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.
- © 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/).
Cite this article
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 -