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The aim is to evaluate how six types of error perturbation affect the performance of various clustering algorithms.

September 1, 1980Psychometrika

An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms

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The aim is to evaluate how six types of error perturbation affect the performance of various clustering algorithms.
Artificial clusters were created with defined internal cohesion and external isolation properties.
Six error perturbations were applied to obscure the true cluster structure.
Performance evaluations were conducted on fifteen clustering algorithms, focusing on sensitivity and recovery.
Hierarchical methods displayed varying sensitivity to different types of error perturbation.
K-means algorithms showed poor recovery when using random seed points.
Alternative starting methods improved recovery performance for nonhierarchical clustering algorithms across all error types.

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Evaluation examines clustering algorithm recovery performance against six error types, revealing variations in sensitivity.

Glenn W. Milligan (1980) studied this question.

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