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:
- K initial "means" are randomly generated within the data domain.
- K clusters are created by associating every observation with the nearest mean. The partitions here represent the Voronoi diagram generated by the means.
- The centroid of each of the k clusters becomes the new mean.
- Repeat steps 2 and 3 are until convergence has been reached.
Advantages of k-means clustering
- Fast, robust and easier to use and understand.
- Relatively efficient: with a large number of variables, k-means may be computationally faster than hierarchical clustering method (if k is small).
- K-means may create tighter clusters than hierarchical clustering, particularly if the clusters are globular.
- Gives best result when data set are distinct or well separated from each other.
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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!