The algorithm successively merges the two most similar clusters based on the distance function until there is one single cluster.
It can generate the non-spherical clusters based on the clusters resulted in the feature space by a kernel-based vector quantization approach.
The AP can adaptively change clusters based on the information obtained during the contention-free period.
Then we used K-means algorithm to acquire multi-level clusters based on the word embedding.
Our goal is to divide these data into clusters based on the two features.
The graph vertices are grouped into clusters based on the edge structure and property [35].
We form super-clusters by grouping clusters based on the conditional geographic distribution of their members.
A. The data is split into 10 clusters based on the spatial distribution of thickness measurements.
Typical iterative bottom up generation of clusters based on the Apriori-principle.
Appropriate design decision can be chosen from the clusters based on the manufacturer׳s market strategy.
(b) K-means result for four clusters based on the length to the six nearest neighbors distance metric.
