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Essential Guide to Clustering Techniques in R for Data Analysis

Discover the essentials of clustering in R, designed for statisticians and data miners. This guide introduces the powerful R programming language, ideal for statistical computing and graphics. Learn about key packages like "cluster" and "fpc", and explore vital functions such as "kmeans" and "hclust". Understand the main steps, including data preparation, applying K-means and hierarchical clustering, and visualization techniques. While R can handle large datasets effectively, it has limitations with nominal attributes and missing values. Elevate your data analysis skills with R!

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Essential Guide to Clustering Techniques in R for Data Analysis

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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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