Approximate single linkage cluster analysis of large data sets in high-dimensional spaces
Computational Statistics & Data Analysis - Special issue on classification
Applied numerical linear algebra
Applied numerical linear algebra
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Bump hunting in high-dimensional data
Statistics and Computing
Computational Statistics & Data Analysis
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Learning Mixtures of Gaussians
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques
Journal of Classification
Document clustering using synthetic cluster prototypes
Data & Knowledge Engineering
Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
Computational Statistics & Data Analysis
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
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Clustering datasets is a challenging problem needed in a wide array of applications. Partition-optimization approaches, such as k-means or expectation-maximization (EM) algorithms, are sub-optimal and find solutions in the vicinity of their initialization. This paper proposes a staged approach to specifying initial values by finding a large number of local modes and then obtaining representatives from the most separated ones. Results on test experiments are excellent. We also provide a detailed comparative assessment of the suggested algorithm with many commonly-used initialization approaches in the literature. Finally, the methodology is applied to two datasets on diurnal microarray gene expressions and industrial releases of mercury.