By Guozhu Dong, James Bailey
''Preface Contrasting is likely one of the most simple kinds of research. Contrasting dependent research is generally hired, usually subconsciously, by means of all kinds of individuals. humans use contrasting to raised comprehend the realm round them and the difficult difficulties they wish to resolve. humans use contrasting to adequately examine the desirability of significant occasions, and to aid them larger stay away from possibly harmful occasions and include almost certainly important ones. Contrasting contains the comparability of 1 dataset opposed to one other. The datasets may possibly signify facts of other time classes, spatial destinations, or periods, or they could characterize info enjoyable diversified stipulations. Contrasting is frequently hired to match instances with a fascinating end result opposed to situations with an bad one, for instance evaluating the benign and diseased tissue sessions of a melanoma, or evaluating scholars who graduate with college levels opposed to those that don't. Contrasting can establish styles that trap alterations and traits over the years or area, or determine discriminative styles that trap modifications between contrasting periods or stipulations. conventional tools for contrasting a number of datasets have been usually extremely simple so they might be played via hand. for instance, it is easy to evaluate the respective characteristic ability, evaluate the respective attribute-value distributions, or examine the respective possibilities of basic styles, within the datasets being contrasted. notwithstanding, the simplicity of such ways has boundaries, because it is tough to exploit them to spot particular styles that supply novel and actionable insights, and establish fascinating units of discriminative styles for construction actual and explainable classifiers''-- Read more...
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Extra resources for Contrast data mining : concepts, algorithms, and applications
Dn |! (n22 + i)! Statistical Measures for Contrast Patterns 17 Smaller values of p are more desirable. When conducting signiﬁcance tests for many contrast patterns, the issue of statistical correction for multiple testing arises. There are various approaches to this problem and a good discussion of the issues by Webb can be found in . Mutual Information: This measures the information shared by the contrast pattern occurrence and the dataset label. It tells us how much knowing whether the contrast pattern occurs reduces our uncertainty about the dataset label and vice versa.
1 Terminology . . . . . . . . . . . . . . . . . . . . . . . Ratio Tree Structure for Mining Jumping Emerging Patterns . Contrast Pattern Tree Structure . . . . . . . . . . . . . . . . . Tree Based Contrast Pattern Mining with Equivalence Classes . Summary and Conclusion . . . . . . . . . . . . . . . . . . . . 1 Introduction 23 24 25 27 28 29 In this chapter we consider the challenge of mining emerging patterns.
In general though, choosing appropriate interestingness measures is very challenging; it is likely to require domain knowledge insights and to require speciﬁcs of the nature of the problem/task at hand. 5 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Terminology . . . . . . . . . . . . . . . . . . . . . . . Ratio Tree Structure for Mining Jumping Emerging Patterns . Contrast Pattern Tree Structure . . . . . . . . . .
Contrast data mining : concepts, algorithms, and applications by Guozhu Dong, James Bailey