Clustering benchmark datasets exploiting the fundamental clustering problems

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 c...

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Vydáno v:Data in brief Ročník 30; s. 105501
Hlavní autoři: Thrun, Michael C., Ultsch, Alfred
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier Inc 01.06.2020
Elsevier
Témata:
algorithms
Cluster analysis
Computer Science
data collection
Dimensionality reduction
eyes
humans
leukemia
Pattern recognition
Projection methods
swarms
Tetragonula
Cluster analysis
Dimensionality reduction
Pattern recognition
Projection methods
ISSN:2352-3409, 2352-3409
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Shrnutí: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].
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ISSN:2352-3409
2352-3409
DOI:10.1016/j.dib.2020.105501