This chapter provides an overview of the uses of cluster methods and describes the procedures involved in conducting cluster analyses.
As a result, evaluating the performance of clustering methods in terms of the functional consistency of the resulting clusters is of great interest.
In other words, the clustering of semiautomatic method provided a better classification result than that of automatic TBSS method.
An excellent overview of cluster methods in theory and in applied research is found in Everitt BS, "Cluster Analysis"[ 26].
As PhyloSub reports a higher number of nodes than Rec-BTP, we were unable to directly compare the provided frequencies of the clusters of each method.
Consequently, utilizing the rapidly increasing functional annotation resources such as Gene Ontology (GO) to improve the performance of clustering methods is of great interest.
We have developed and applied the approach of "signature networks" which overcomes some of the drawbacks of clustering methods.
(2) We apply three different clustering algorithms in this work and analyze the effect of clustering methods on the performance of keyphrase extraction.
In a short time the Q function evaluating the goodness of partitioning a graph into given clusters became an essential element of a wide range of clustering methods.
Finally, some numerical examples are provided and results show the efficiency of cluster synchronization methods in Boolean network.
