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Data Mining A Tutorial Based Primer 2nd Edition by Richard Riger ISBN 9781498763974 1498763979

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ISBN 10: 1498763979
ISBN 13: 9781498763974
Author: Richard J. Roiger

Data Mining
A Tutorial-Based Primer, Second Edition

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.    

Data Mining A Tutorial Based Primer 2nd Table of contents:

 

Here is a summarized version of the table of contents for the “Data Mining: A Practical Approach” book:

Section I: Data Mining Fundamentals

  • Chapter 1: Data Mining: A First View

    • Key Topics: Data Science, Analytics, Data Mining, Knowledge Discovery; Supervised & Unsupervised Learning; Decision Trees; Data Mining vs Data Query; Process Model for Data Mining; Big Data, Cloud Computing, and Ethics.

  • Chapter 2: Data Mining: A Closer Look

    • Key Topics: Data Mining Strategies; Supervised Data Mining Techniques; Rule-Based Methods; Neural Networks; Regression; Clustering; Evaluating Performance of Models.

  • Chapter 3: Basic Data Mining Techniques

    • Key Topics: Decision Trees; Association Rules; K-means Algorithm; Genetic Learning; Choosing Data Mining Techniques.

Section II: Tools for Knowledge Discovery

  • Chapter 4: Weka—An Environment for Knowledge Discovery

    • Key Topics: Getting Started with Weka; Building Decision Trees; Attribute Selection; Clustering with K-means.

  • Chapter 5: Knowledge Discovery with RapidMiner

    • Key Topics: Using RapidMiner; Decision Trees; Rule Generation; Cross-Validation; Unsupervised Clustering.

  • Chapter 6: The Knowledge Discovery Process

    • Key Topics: Goal Identification, Data Preprocessing, Transformation, Data Mining, Interpretation, Evaluation, CRISP-DM Process.

  • Chapter 7: Formal Evaluation Techniques

    • Key Topics: Evaluating Models; Comparing Performance of Learners; Confidence Intervals; Unsupervised Evaluation.

Section III: Building Neural Networks

  • Chapter 8: Neural Networks

    • Key Topics: Feed-Forward Neural Networks; Training Neural Networks (Backpropagation, Genetic Learning); Self-Organizing Maps (SOM); Neural Network Explanation.

  • Chapter 9: Building Neural Networks with Weka

    • Key Topics: Modeling XOR Function; Satellite Image Data Mining; Unsupervised Clustering with Neural Networks in Weka.

  • Chapter 10: Building Neural Networks with RapidMiner

    • Key Topics: XOR Function Modeling; Predicting Customer Churn; Self-Organizing Maps in RapidMiner.

Section IV: Advanced Data Mining Techniques

  • Chapter 11: Supervised Statistical Techniques

    • Key Topics: Naïve Bayes Classifier; Support Vector Machines; Linear and Logistic Regression; Regression Trees.

  • Chapter 12: Unsupervised Clustering Techniques

    • Key Topics: Agglomerative Clustering; Conceptual Clustering; Expectation Maximization; Genetic Algorithms.

  • Chapter 13: Specialized Techniques

    • Key Topics: Time-Series Analysis; Web Mining; Text Mining; Handling Large, Imbalanced, and Streaming Data; Ensemble Techniques (Bagging, Boosting).

  • Chapter 14: The Data Warehouse

    • Key Topics: Data Warehouse Design; OLAP; Excel Pivot Tables for Analytics; Decision Support Systems.

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