the cmeans algorithm module
Fuzzy clustering (also referred to as soft clustering) is a form of clustering in which each data point can belong to more than one cluster.
Clustering Or cluster analysis involves assigning data points to clusters (also called buckets, bins, Or classes), Or homogeneous classes, such that items in the same class Or cluster are as similar as possible, while items belonging to different classes are as dissimilar as possible. Clusters are identified via similarity measures. These similarity measures include distance, connectivity, And intensity. Different similarity measures may be chosen based on the data Or the application.
https://en.wikipedia.org/wiki/Fuzzy_clustering
Clustering problems have applications in biology, medicine, psychology, economics, and many other disciplines.
Bioinformatics
In the field of bioinformatics, clustering Is used for a number of applications. One use Is as a pattern recognition technique to analyze gene expression data from microarrays Or other technology. In this case, genes with similar expression patterns are grouped into the same cluster, And different clusters display distinct, well-separated patterns of expression. Use of clustering can provide insight into gene function And regulation. Because fuzzy clustering allows genes to belong to more than one cluster, it allows for the identification of genes that are conditionally co-regulated Or co-expressed. For example, one gene may be acted on by more than one Transcription factor, And one gene may encode a protein that has more than one function. Thus, fuzzy clustering Is more appropriate than hard clustering.
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