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Data Mining

Richard J. Roiger
€83.99
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advanced data mining tutorials
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algorithms
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Credit Card Insurance
Credit Card Promotion Database
Data Mining
Data Mining Algorithms
Data Mining Techniques
Data Mining Tool
data preprocessing techniques
data processing
Data Set
Data Warehouse
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Genetic Learning
Hidden Layer
intelligent agent systems
knowledge discovery process
knowledge-based systems
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Life Insurance Promotion
Magazine Promotion
Market Basket Analysis
modeling building
Neural Network
neural network modelling
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Performance Vector
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rule-based inference systems
Satellite Image Data Set
softlaunch
Spam Data Set
statistical learning methods
Supervised Learner Model
Test Set Accuracy
Test Set Instances
Unsupervised Clustering
visualization
Weka
XOR Function

Product details

  • ISBN 9781498763974
  • Weight: 936g
  • Dimensions: 178 x 254mm
  • Publication Date: 01 Dec 2016
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
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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.
About Richard J. Roiger

Richard J. Roiger is a professor emeritus at Minnesota State University, Mankato where he taught and performed research in the Computer & Information Science Department for 27 years. Dr. Roiger’s Ph.D. degree is in Computer & Information Sciences from the University of Minnesota. Dr. Roiger continues to serve as a part-time faculty member teaching courses in data mining, artificial intelligence and research methods. Richard enjoys interacting with his grandchildren, traveling, writing and pursuing his musical talents.

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