The motif analysis is useful because it can be used to divide clusters into smaller classes with more narrow functions.
We can further divide Clusters II and III into sub-clusters.
If this is true, then 1. identifiers whose removal leads to the division of a cluster into two, where the two divided clusters have improved 'quality' relative to the initial cluster are potentially bad.
The existing Cluster Identifier of the dividing cluster, or of the cluster to where the new one is attached, is kept unchanged becoming a Virtual Cluster.
The KM-I2C algorithm divides distinct clusters in datasets into sub clusters based on inter-cluster and intra-cluster similarities.
The BICLIC divides the clustering problem into individual sample clustering problems.
While the conventional clustering method divides observation into clusters, the VARCLUS produces readily interpretable disjoint clusters [ 27].
The existing techniques that aggregate data are divided into cluster based, non-cluster based and other approaches.
The resultant network can be divided into clusters.
The nodes of each layer are divided into clusters.
