Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures

The objective interestingness measures play an important role in data mining because they are used for mining, filtering and ranking the patterns. However, there is no research that collects the measures fully as well as there is no tool that can: automatically calculate the interestingness values of the patterns by using those measures, and is the framework for rapidly developing the applications related to objective interestingness measures. This paper describes Interestingnesslab - a tool of the objective interestingness measures is developed in the R language. The main functions of the tool are: mining a set of association rules and presenting them by the cardinalities ( n,n_X,n_Y,n_XY ), calculating the interestingness value of an association rule according to 1 of 109 collected measures; calculating the interestingness values of the whole rule set in many measures selected by the user; discovering the tendencies in a data set and recommending the top N items to the user; and studying the specific behavior of a set of interestingness measures in the context of a specific dataset and in an exploratory data analysis perspective. With Interestingnesslab, the user can easily and quickly reuse its functions to develop his/her own applications.