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Clustering Toolbox 1.0

  Date Added: July 12, 2013  |  Visits: 273

Clustering Toolbox

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The purpose of the development of this toolbox was to compile a continuously extensible, standard tool, which is useful for any MATLAB user for one's aim. In Chapter 1 of the downloadable related documentation one can find a theoretical introduction containing the theory of the algorithms, the definition of the validity measures and the tools of visualization, which help to understand the programmed MATLAB files.Chapter 2 deals with the exposition of thefiles and the description of the particular algorithms, and they are illustrated with simple examples, while in Chapter 3 the wholeToolbox is tested on real data sets during the solution of three clustering problems: comparison and selection of algorithms; estimating the optimal number of clusters; and examiningmultidimensional data sets.About the ToolboxThe Fuzzy Clustering and Data Analysis Toolbox is a collection of MATLAB functions. The toolbox provides five categories of functions:- Clustering algorithms. These functions group the given data set into clusters by different approaches: functions Kmeans and Kmedoidare hard partitioning methods, FCMclust, GKclust, GGclust are fuzzy partitioning methods with different distance norms.- Evaluation with cluster prototypes. On the score of the clustering results of a data set there is a possibility to calculate membership for "unseen" data sets with these set of functions. In 2-dimensional case the functions draw a contour-map in the data space to visualizethe results.- Validation. The validity function provides cluster validity measures for each partition. It is useful when the number of cluster is unknown a priori. The optimal partition can be determined by the point of the extrema of the validation indexes in dependence of the number of clusters. The indexes calculated are: Partition Coefficient (PC), Classification Entropy (CE), Partition Index (SC), Separation Index (S), Xie and Beni's Index (XB), Dunn's Index (DI) and Alternative Dunn Index (DII).- Visualization. The Visualization part of this toolbox provides the modified Sammon mapping of the data. This mapping method is amultidimensional scaling method described by Sammon.- Examples. An example based on industrial data set to present the usefulness of these toolbox and algorithms.

Requirements: No special requirements
Platforms: Matlab
Keyword: Calculate Contourmap Dimensional Function Kmeans Kmedoidare Membership Methods Partition Partitioning Possibility Prototypes Quotunseenquot Results Score Space Validation Visualizethe
Users rating: 0/10

License: Freeware Size: 2.05 MB
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