Contrast data mining
Subtitle: concepts, algorithms, and applications
By: edited by Guozhu Dong and James Bailey
Series: Chapman & Hall/CRC data mining and knowledge discovery series
Publisher: CRC Press, Taylor & Francis Group; Chapman and Hall/CRC
Published: 2013
Place of publication: Boca Raton
Language: eng
Number of pages: 410
ISBN-13: 9781439854327
Subjects: Contrast data mining; COMPUTERS / Database Management / Data Mining; COMPUTERS / Programming / Algorithms; BUSINESS & ECONOMICS / Statistics
Description: "Preface Contrasting is one of the most basic types of analysis. Contrasting based analysis is routinely employed, often subconsciously, by all types of people. People use contrasting to better understand the world around them and the challenging problems they want to solve. People use contrasting to accurately assess the desirability of important situations, and to help them better avoid potentially harmful situations and embrace potentially beneficial ones. Contrasting involves the comparison of one dataset against another. The datasets may represent data of different time periods, spatial locations, or classes, or they may represent data satisfying different conditions. Contrasting is often employed to compare cases with a desirable outcome against cases with an undesirable one, for example comparing the benign and diseased tissue classes of a cancer, or comparing students who graduate with university degrees against those who do not. Contrasting can identify patterns that capture changes and trends over time or space, or identify discriminative patterns that capture differences among contrasting classes or conditions. Traditional methods for contrasting multiple datasets were often very simple so that they could be performed by hand. For example, one could compare the respective feature means, compare the respective attribute-value distributions, or compare the respective probabilities of simple patterns, in the datasets being contrasted. However, the simplicity of such approaches has limitations, as it is difficult to use them to identify specific patterns that offer novel and actionable insights, and identify desirable sets of discriminative patterns for building accurate and explainable classifiers"--
Notes: Includes bibliographical references (pages 363-402) and index.