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Discovering Knowledge in Data: An Introduction to Data Mining
Learn Data Mining by doing data mining
Data 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 techniques
Complete 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.
Data 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 techniques
Complete 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.
- GenresSoftware
222 pages, Hardcover
First published November 18, 2004
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Displaying 1 - 2 of 2 reviews
March 18, 2019
Mandatory read for school, did not finish around 20%.
The chapters are easy to follow. The chapter on Data preprocessing was very valuable to me, explaining why it's so important and which (statistical) methods could be used.
Unfortunately the way the results were being obtained (old version of SPSS) were not so relevant for me since in this course I have to use Python. It does seem like a good book to learn how Data Mining algorithms work and how to interpret its results.
The chapters are easy to follow. The chapter on Data preprocessing was very valuable to me, explaining why it's so important and which (statistical) methods could be used.
Unfortunately the way the results were being obtained (old version of SPSS) were not so relevant for me since in this course I have to use Python. It does seem like a good book to learn how Data Mining algorithms work and how to interpret its results.
January 21, 2018
Really got me interested in the domain of data mining. It is rather an old book and I would have liked some more hands-on examples in a more modern mining tool. Nevertheless, worth the read.
Displaying 1 - 2 of 2 reviews