Akaike’s Information Criterion for Linearly Separable Clusters
Copyright policy ) Roberto N. Padua; Maria Eda B. Arado;
Akaike’s Information Criterion for Linearly Separable Clusters
Using the Akaike Information Criterion (AIC) in cluster analysis with linearly separable components, the paper demonstrates the superiority of using the vector of slopes as inputs to the K-Means algorithm over using the raw data in determining the number of clusters. Keywords - AIC(Akaike’s Information Criterion), Kullback-Leibler information, cluster analysis, linear separability
- Mindanao University of Science and Technology Philippines
- Liceo de Cagayan University Philippines
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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