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Cluster Validation Based on Fisher’s Linear Discriminant Analysis

Kächele, Fabian ORCID iD icon 1; Schneider, Nora 2
1 Institut für Operations Research (IOR), Karlsruher Institut für Technologie (KIT)
2 Karlsruher Institut für Technologie (KIT)

Abstract:

Cluster analysis aims to find meaningful groups, called clusters, in data. The objects within a cluster should be similar to each other and dissimilar to objects from other clusters. The fundamental question arising is whether found clusters are “valid clusters” or not. Existing cluster validity indices are computation-intensive, make assumptions about the underlying cluster structure, or cannot detect the absence of clusters. Thus, we present a new cluster validation framework to assess the validity of a clustering and determine the underlying number of clusters k . Within the framework, we introduce a new merge criterion analyzing the data in a one-dimensional projection, which maximizes the ratio of between-cluster- variance to within-cluster-variance in the clusters. Nonetheless, other local methods can be applied as a merge criterion within the framework. Experiments on synthetic and real-world data sets show promising results for both the overall framework and the introduced merge criterion

Zugehörige Institution(en) am KIT Institut für Operations Research (IOR)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 03.2025
Sprache Englisch
Identifikator ISSN: 0176-4268, 1432-1343
KITopen-ID: 1000172621
Erschienen in Journal of Classification
Verlag Springer
Band 42
Heft 1
Seiten 54–71
Vorab online veröffentlicht am 04.07.2024
Nachgewiesen in Web of Science
Dimensions
OpenAlex
Scopus
Globale Ziele für nachhaltige Entwicklung Ziel 10 – Weniger Ungleichheiten

Verlagsausgabe §
DOI: 10.5445/IR/1000172621
Veröffentlicht am 22.07.2024
Seitenaufrufe: 58
seit 22.07.2024
Downloads: 38
seit 25.07.2024
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