To Cluster, or Not to Cluster: An Analysis of Clusterability Methods

Abstract

Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. For most applications, applying clustering is only appropriate when cluster structure is present. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis. However, methods for evaluating clusterability vary radically, making it challenging to select a suitable measure. In this paper, we perform an extensive comparison of measures of clusterability and provide guidelines that clustering users can reference to select suitable measures for their applications.

30 pages, 3 figures, 10 tables

References in corpus (2)

• Analysis of PCA Algorithms in Distributed Environments
• Macros to Conduct Tests of Multimodality in SAS

Cited by in corpus (11)

• How to Use K-means for Big Data Clustering?
• DenMune: Density peak based clustering using mutual nearest neighbors
• An Internal Cluster Validity Index Using a Distance-based Separability Measure
• Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning
• The UU-test for Statistical Modeling of Unimodal Data
• Rank-transformed subsampling: inference for multiple data splitting and exchangeable p-values
• Clusterability test for categorical data
• Onset of a conceptual outline map to get a hold on the jungle of cluster analysis
• A Comparative Study on Structural and Semantic Properties of Sentence Embeddings
• Dataset-Adaptive Dimensionality Reduction
• Hybrid Quantum-Classical Clustering for Preparing a Prior Distribution of Eigenspectrum