Unsupervised K-Means Clustering Algorithm
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Summary
An unsupervised learning schema is constructed for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters.
- Type
- article
- Published
- 2020-04-20
- Cited by
- 1,833
- References
- 39
- Access
- Open access
- OpenAlex
- https://openalex.org/W3019913914
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:218597833
Keywords
Computer science, Cluster analysis, Artificial intelligence, Canopy clustering algorithm, Unsupervised learning
References
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- Finding Groups in Data: An Introduction to Cluster Analysis
- X-means: Extending K-means with Efficient Estimation of the Number of Clusters
- Algorithms for Clustering Data
- Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions
- Cluster validation using graph theoretic concepts
- Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
- A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
- External validation measures for K-means clustering: A data distribution perspective
- Data clustering: 50 years beyond K-means
- Unsupervised Learning of Finite Mixture Models
- A Cluster Separation Measure
- A Survey on Internal Validity Measure for Cluster Validation
- A dendrite method for cluster analysis
- Graph Regularized Nonnegative Matrix Factorization for Data Representation
- Some methods for classification and analysis of multivariate observations
- On Clustering Validation Techniques
- Modified Dunn's cluster validity index based on graph theory
- A robust EM clustering algorithm for Gaussian mixture models
- Clustering by fast search and find of density peaks
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