Chapter 18 Hi Ho, Hi Ho — Data Mining We Go
Data mining is an area of research and practice that is focused on discovering novel patterns in data. As usual, R has lots of possibilities for data mining. In this chapter we will begin experimentation with essential data mining techniques by trying out one of the easiest methods to understand: association rules mining. More beer and diapers please!
Sources.
Reference
Michael Hahsler, Kurt Hornik, and Thomas Reutterer (2006) Implications of probabilistic data modeling for mining association rules. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nuernberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, Studies in Classification, Data Analysis, and Knowledge Organization, pages 598–605. Springer-Verlag.
R Functions Used in this Chapter
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apriori()- Uses the algorithm of the same name to analyze a transaction data set and generate rules. -
itemFrequencyPlot()- Shows the relative frequency of commonly occurring items in the spare occurrence matrix. -
inspect()- Shows the contents of the data object generated byapriori()that generates the association rules. -
install.packages()- Loads package from the CRAN respository. -
summary()- Provides an overview of the contents of a data structure.
