Clustering benchmark datasets exploiting the fundamental clustering problems

Michael C Thrun; Alfred Ultsch

doi:10.1016/j.dib.2020.105501

Clustering benchmark datasets exploiting the fundamental clustering problems

Data Brief. 2020 Apr 20:30:105501. doi: 10.1016/j.dib.2020.105501. eCollection 2020 Jun.

Authors

Michael C Thrun^{1

2}, Alfred Ultsch¹

Affiliations

¹ Databionics Research Group, Philipps-University of Marburg, Hans-Meerwein-Straße 6, D-35032 Marburg, Germany.
² Dept. of Hematology, Oncology and Immunology, Philipps-University Marburg, Germany.

Abstract

The Fundamental Clustering Problems Suite (FCPS) offers a variety of clustering challenges that any algorithm should be able to handle given real-world data. The FCPS consists of datasets with known a priori classifications that are to be reproduced by the algorithm. The datasets are intentionally created to be visualized in two or three dimensions under the hypothesis that objects can be grouped unambiguously by the human eye. Each dataset represents a certain problem that can be solved by known clustering algorithms with varying success. In the R package "Fundamental Clustering Problems Suite" on CRAN, user-defined sample sizes can be drawn for the FCPS. Additionally, the distances of two high-dimensional datasets called Leukemia and Tetragonula are provided here. This collection is useful for investigating the shortcomings of clustering algorithms and the limitations of dimensionality reduction methods in the case of three-dimensional or higher datasets. This article is a simultaneous co-submission with Swarm Intelligence for Self-Organized Clustering [1].

Keywords: Cluster analysis; Dimensionality reduction; Pattern recognition; Projection methods.