Data Mining: Practical Machine Learning Tools and TechniquesData Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html. It contains - Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book - Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book - Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc. - Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects - Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface - Includes open-access online courses that introduce practical applications of the material in the book |
Contents
II More advanced machine learning schemes | 205 |
Theoretical foundations | 533 |
The WEKA workbench | 553 |
| 573 | |
Common terms and phrases
applied association rules attribute values autoencoder Bayesian network Chapter class value classifier clustering compute conditional random fields corresponding cross-validation data mining dataset decision tree deep learning described discretization distribution document ensemble evaluation example factor graph FIGURE filter Gaussian given gradient descent hidden humidity implementation input instance-based learning item sets iteration kernel layer learner learning algorithm learning scheme linear models linear regression logistic regression loss function machine learning Markov missing values model trees multi-instance multiclass Naïve Bayes neural network node nominal attributes normal numeric attributes Outlook output overfitting package parameters perceptron performance principal component analysis probabilistic probability estimates problem produce pruning random variable result rule set sample Section selection simple split statistical stochastic gradient descent structure subset subtree support vector machines Table techniques temperature test instance test set tion training data training instances training set transformed two-class weather data weights WEKA windy zero