Data Mining - Challenges, Models, Methods and Algorithms (2003)
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@MISC{Hegland03datamining,
author = {Markus Hegland},
title = {Data Mining - Challenges, Models, Methods and Algorithms},
year = {2003}
}
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Abstract
Contents 1 Introduction 5 2 Modelling: Objects, Features and Distributions 11 2.1 Objects and Properties . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Data Sets, Distributions and Measures . . . . . . . . . . . . . 14 2.3 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4 Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5 Restricted Observations . . . . . . . . . . . . . . . . . . . . . 19 2.6 Properties and Patterns . . . . . . . . . . . . . . . . . . . . . 19 3 Association rules 21 3.2 Modelling for association rules . . . . . . . . . . . . . . . . . . 27 3.2.1 Modelling the Data . . . . . . . . . . . . . . . . . . . . 27 3.2.2 Modelling the Patterns in the Database . . . . . . . . . 29 3.3 Structure of X . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.4 Structure of the Predicates on X . . . . . . . . . . . . . . . . . 43 3.5 Support and antimonotonicity . . . . . . . . . . . . . . . . . . 47 3.6 Expectation and the
