This is a tutorial on R and Data Mining at the AusDM 2018 Conference, Charles Sturt University, Bathurst, Australia, 29 November 2018.

Prerequisite

• Familiarity with scripting and/or programming
• Basic knowledge of data mining, especially of,
  • Association rules
    https://en.wikipedia.org/wiki/Association_rule_learning
  • Text mining
    https://en.wikipedia.org/wiki/Text_mining
  • Social network analysis (SNA)
    https://en.wikipedia.org/wiki/Social_network_analysis

Requirement

You will need to bring your own laptop. Please install the required software and R packages and download the datasets, slides and scripts below before coming to the course.

• Software and Packages
• R
  http://www.r-project.org/
• RStudio (desktop edition)
  http://www.rstudio.com/products/rstudio/download/
• R packages (please run the R script to install required R packages)
  http://www.rdatamining.com/books/rdm/code/Install-R-packages.R
• RStudio project archive [MLwR.zip], which contains all datasets, slides and scripts below. Alternatively, you may download individual files separately at links below.  
• Datasets
  • Titanic dataset
    http://www.rdatamining.com/data/titanic.raw.rdata
  • Twitter dataset
    http://www.rdatamining.com/data/RDataMining-Tweets-20160212.rds
  • Graph dataset
  http://www.rdatamining.com/data/graph.rdata
• Slides
  • R Programming: [PDF]
  • Association Rule Mining with R: [PDF]
  • Text Mining with R: [PDF]
  • Social Network Analysis with R: [PDF]
• R scripts [ZIP]

Outline

This is a tutorial on machine learning with R. It will cover four sessions below. Each session will be of 45 minutes, composed of a 35-minute tutorial and a 10-minute lab.

• R Programming:
  basics of R language and programming, parallel computing, and data import and export
• Association Rule Mining with R:
  mining and selecting interesting association rules, redundancy removal, and rule visualisation
• Text Mining with R:
  text mining, word cloud, topic modelling, and sentiment analysis,
• Social Network Analysis with R:
  graph construction, graph query, centrality measures, and graph visualisation