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Contrast data mining : concepts, algorithms, and applications / edited by Guozhu Dong and James Bailey.

Contributor(s): Material type: TextTextSeries: Chapman & Hall/CRC data mining and knowledge discovery seriesPublication details: Boca Raton : CRC Press, 2013.Description: xxiv, 410 p. : ill. ; [ca. 23-29] cmISBN:
  • 9781439854327
Subject(s): LOC classification:
  • QA76.9.D343 C65 2013
Available additional physical forms:
  • Also available as an electronic resource.
Contents:
1. Preliminaries and statistical contrast measures -- 2. Contrast mining algorithms -- 3. Generalized contrasts, emerging data cubes, and rough sets -- 4. Contrast mining for classification & clustering -- 5. Contrast mining for bioinformatics and chemoinformatics -- 6. Contrast mining for special domains -- 7. Survey of other papers.
Summary: "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"-- Provided by publisher.
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"A Chapman & Hall book."

Includes bibliographical references (p. 363-402) and index.

1. Preliminaries and statistical contrast measures -- 2. Contrast mining algorithms -- 3. Generalized contrasts, emerging data cubes, and rough sets -- 4. Contrast mining for classification & clustering -- 5. Contrast mining for bioinformatics and chemoinformatics -- 6. Contrast mining for special domains -- 7. Survey of other papers.

"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"-- Provided by publisher.

Also available as an electronic resource.

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