In statistics and data mining, hierarchical clustering (or hierarchical cluster analysis, HCA) is a method of cluster analysis which aims to build a hierarchy of clusters. This method produces a hierarchical decomposition of a given set of data objects. Now, bioinformaticians at Creative Proteomics are proud to tell you we are open to help you with Hierarchical Clustering Analysis Service!
We can classify hierarchical methods on the basis of how the hierarchical decomposition is formed. Strategies for hierarchical clustering generally can be divided into two types:
Basic agglomerative hierarchical clustering algorithm
Simple divisive algorithm (divisive techniques are less common):
Hierarchical clustering has the clear advantage that any valid measure of distance can be applied. In fact, the observations themselves are not required: all that is used is a matrix of distances.
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As one of the leading omics industry company in the world! Creative Proteomics now is opening to provide hierarchical clustering service for our customers. With over 8 years experience in the field of bioinformatics, we are willing to provide our customer the most outstanding service! Contact us for all the detailed informations!
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