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Clustering in R

Clustering in R. Xue li CS548 showcase. Source. http://www.statmethods.net/advstats/cluster.html http://www.r-project.org/ http://cran.r-project.org/web/packages/cluster/index.html http://cran.r-project.org/web/packages/. Introduction to R.

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Clustering in R

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  1. Clustering in R Xue li CS548 showcase

  2. Source • http://www.statmethods.net/advstats/cluster.html • http://www.r-project.org/ • http://cran.r-project.org/web/packages/cluster/index.html • http://cran.r-project.org/web/packages/

  3. Introduction to R R is a free software programming language and software environment for statistical computing and graphics. (From Wikipedia) For two kinds of people: Statisticians and data miners Two main applications: Developing statistical tools, Data analysis

  4. If you have learned any other programming language, it will be very easy to handle R. • If you don’t, R will be a good start

  5. Package and function • http://cran.r-project.org/web/packages/available_packages_by_name.html

  6. Clustering • Package: “cluster”, “fpc”… • Functions: “kmeans”, “dist”, “daisy”,“hclust”…

  7. Main steps • Data preparation (missing value, nominal attribute…) • K-means • Hierarchical • Plotting/Visualization • Validating/Evaluation

  8. disadvantage • Cannot handle nominal attributesand missing values directly • Cannot provide evaluating matrix directly

  9. Advantage • Can handle large dataset • Write our own functions (Easier than Java in Weka)

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