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*   Fuzzy Clustering
Cluster analysis is a technique for grouping data into clusters to find common structural features in spectral data. Membership degrees between zero and one are used in fuzzy clustering, instead of crisp assignments of the data to clusters. Fuzzy clustering is based on the dot-product distance between the center of clusters and experimental spectral points. The dot-product is calculated from mass spectral n-dimensional space with the intensities being the coordinates and m/z values dimensions. The number of peaks whose intensities are above the predefined threshold determines the dimensionality of spectral space. The number of clusters is usually pre-defined. For additional information, see Spectra Projector Module.
Fuzzy clustering example


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Related Topics:
  Principal Component Analysis (PCA)
  Self-Organizing Maps
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