A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
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Summary
It is demonstrated that popular initialization methods often perform poorly and that there are in fact strong alternatives to these methods, and eight commonly used linear time complexity initialization methods are compared.
- Type
- article
- Published
- 2012-09-10
- Cited by
- 1,226
- References
- 89
- Access
- Open access
- OpenAlex
- https://openalex.org/W2059515884
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:6954668
Keywords
Initialization, Computer science, Cluster analysis, Gradient descent, Algorithm
References
- Making k-means Even Faster
- Computational experiences with the exchange method
- 海外情報 Georgia Institute of Technologyでの研究
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- Advances in neural information processing systems 7
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- A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems
- External validation measures for K-means clustering: A data distribution perspective
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