Tutorial at AusDM 2018

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

Contact

If you have any questions or feedback, please do not hesitate to contact me on yanchang <at> RDataMining.com. Thanks.