Applied Data Mining: Statistical Methods for Business and Industry

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Intelligent heart disease prediction system using data mining techniques (2008, background): “Giudici defines it as “a process of selection, exploration and modelling of large quantities of data to discover regularities or relations that are at first unknown with the aim of obtaining clear and useful results for the owner of database” [5].”
Predictive data mining in clinical medicine: Current issues and guidelines (2008, background): “Data mining is the process of selecting, exploring and modeling large amounts of data in order to discover unknown patterns or relationships which provide a clear and useful result to the data analyst [1].”
An up-to-date comparison of state-of-the-art classification algorithms (2017, unknown): “…break-even point, F-measure ( Rijsbergen, 1979 ), Squared Error, Cross-Entropy, Kolmogorov-Smirnov statistic, Cohen’s κ ( Carletta, 1996 ), Lift ( Giudici, 2005 ), Q-statistic ( Yule, 1900 ), Brier score ( Hernández-Orallo, Flach, & Ferri, 2012 ), H-measure ( Hand, 2009 ), Average/Relative…”
A review of data mining applications for quality improvement in manufacturing industry (2011, methodology): “Next, selected data is put into a tabular format in which instances and variables take place in rows and columns, respectively (Giudici, 2003).”