Sieving Data
Topics: data mining. Market basket analysis. Understanding consumer behaviour. Association rules or what is behind recommendation systems. data mining. Market basket analysis. Understanding consumer behaviour. Association rules or what is behind recommendation systems. Dimension reduction. Multidimensional scaling. Factorial analysis, Component analysis (principal, simple, multiple). Linear discriminant analysis. Feature selection.
Code: R / Tool: RStudio
Hi! Ho! Hi! Ho! Data mining
Market basket analysis. Understanding consumer behaviour. Association rules or what is behind recommendation systems (apriori algorithm).
Consult the case in a new tab (Show/Hide All Code in the case upper-right corner) and the notes in a new tab (Show/Hide All Code in the case upper-right corner).
Market Basket Analysis with Python
We need the mlxtend module and its own apriori algorithm. Here is an example.
Data wrangling (with the R dplyr package)
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Dimension reduction methods
Dimension reduction. Multidimensional scaling. Factorial analysis, Component analysis (principal, simple, multiple). Linear discriminant analysis. Feature selection.
Consult the case in a new tab (Show/Hide All Code in the case upper-right corner) and the notes in a new tab.
A doctoral candidate in behavioral ecology of Université du Québec à Montréal (UQAM) work with Behaviour Interactive, a video game maker, on a research project.
They study behaviours during massive multiplayer online game (MMOG). The models applied during the study of online players were originally designed to study crickets!
Both ‘species’ are confronted to the same challenges: competition for scarce resources, predation, cooperation techniques. Data mining techniques and behavioral evolution theories can be used to analyze and make sound prediction about online players. The data collected from highly complex and changing environments (yet, under control) can help explaining evolutionary behaviours. In conclusion, the experiement was benefitial to both fields.