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Since modularity clustering generates large number of clusters in general, we need to have additional phase to merge clusters more.
Cluster merge phase Since modularity clustering generates large number of clusters in general, we need to have additional phase to merge clusters more.
Our results tend to support the conjecture that spatial clustering generates higher long-run knowledge growth rates in industries characterized by highly tacit knowledge, while the opposite is true when the degree of codification is important.
K-means clustering generates a specific number of disjoint (non-hierarchal) clusters.
However, the added value provided is that genetic-based clustering generates the highest possible number of clusters whose cluster centers attained the highest fitness value.
Hierarchical clustering generates less distinct clusters with considerably lower linkage cost (final linkage cost 1,400 vs 7,000) for the emotional arcs from nonsense books, and the winning node vectors on a self-organizing map lack coherent structure (see Figures S26 and S29 in Appendix H in Additional file 1).
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We can observe that this clustering generated four major clusters.
Unsupervised hierarchical clustering generated four major clusters.
We performed 10,000 bootstrap clustering generating 4 clusters for each round of bootstrapping.
Hierarchical clustering generated six distinct clusters of genes with similar gene expression patterns across all species.
The MH adjustment assuming incidental clustering generated a final model very similar to GEE.
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