K-means clustering is a method of vector quantization, resulting from signal processing, that is popular for cluster analysis in data mining. K-means clustering aims to divide n objects into k clusters in which each object belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a separation of the data space into Voronoi cells. Now, bioinformaticians at Creative Proteomics are proud to offer our customers Hierarchical Clustering Analysis service.
Demonstration of the standard algorithm for k-means clustering:
Advantages of k-means clustering
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As one of the leading omics industry company in the world! Creative Proteomics now is opening to provide k-means 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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