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Discovering Knowledge in Data

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  • 240 stron
  • 9 godzin czytania

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Learn Data Mining by doing data miningData mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics * Data preprocessing and classification* Exploratory analysis* Decision trees* Neural and Kohonen networks* Hierarchical and k-means clustering* Association rules* Model evaluation techniquesComplete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.

Zakup książki

Discovering Knowledge in Data, Daniel T. Larose

Język
Rok wydania
2004
Oprawa
(twarda)
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3,8
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Tytuł
Discovering Knowledge in Data
Język
angielski
Rok wydania
2004
Oprawa
twarda
Liczba stron
240
ISBN10
0471666572
ISBN13
9780471666578
Seria
Tagi
Ocena
3,8 z 5
Opis
Learn Data Mining by doing data miningData mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics * Data preprocessing and classification* Exploratory analysis* Decision trees* Neural and Kohonen networks* Hierarchical and k-means clustering* Association rules* Model evaluation techniquesComplete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.