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Data Mining: A Tutorial-Based Primer 2/e

作者：Richard J. Roiger
原價：NT$ 1,400

ISBN：9781498763974
版次：2
年份：2017
出版商：CRC Press
頁數/規格：487頁/平裝單色
參考網頁：Data Mining: A Tutorial-Based Primer 2/e

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Description
Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools.

Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more.

The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.

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Features

Provides in-depth coverage of basic and advanced topics in data mining and knowledge discovery

Presents the most popular data mining algorithms in an easy to follow format

Includes instructional tutorials on applying the various data mining algorithms

Provides several interesting datasets ready to be mined

Offers in-depth coverage of RapidMiner Studio and Weka’s Explorer interface

Teaches the reader (student,) hands-on, about data mining using RapidMiner Studio and Weka

Gives instructors a wealth of helpful resources, including all RapidMiner processes used for the tutorials and for solving the end of chapter exercises. Instructors will be able to get off the starting block with minimal effort

Extra resources include screenshot sequences for all RapidMiner and Weka tutorials and demonstrations, available for students and instructors alike

Table of Contents
Section I Data Mining Fundamentals
1. Data Mining: A First View
2. Data Mining: A Closer Look
3. Basic Data Mining Techniques

Section II Tools for Knowledge Discovery
4. Weka—An Environment for Knowledge Discovery
5. Knowledge Discovery with RapidMiner
6. The Knowledge Discovery Process
7. Formal Evaluation Techniques

Section III Building Neural Networks
8. Neural Networks
9. Building Neural Networks with Weka
10. Building Neural Networks with RapidMiner

Section IV Advanced Data Mining Techniques
11. Supervised Statistical Techniques
12. Unsupervised Clustering Techniques
13. Specialized Techniques
14. The Data Warehouse

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