Data Mining: Practical Machine Learning Tools and Techniques, Second EditionData Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.
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Contents
| 41 | |
Knowledge representation | 61 |
Part II | 80 |
4 | 83 |
2 | 89 |
5 | 91 |
5 | 143 |
1 | 148 |
The Explorer | 369 |
Doing it again | 377 |
Training and testing learning schemes | 384 |
Clustering and association rules | 391 |
Unsupervised instance filters | 400 |
The Knowledge Flow interface | 427 |
Analyzing the results | 440 |
2 | 449 |
Common terms and phrases
applied association rules astigmatic attribute values Bayesian networks calculate called Chapter choose class value classifier contact lens cost coverage cross-validation data mining database dataset decision tree described distribution error rate estimate evaluate example Figure humidity hypermetrope hyperplane hyperrectangle input instance space instance-based learning involved Iris setosa Iris versicolor Iris virginica kD-tree leaf learner learning algorithms learning scheme linear models linear regression logistic regression loss function machine learning measure missing values model tree myope Naïve Bayes node nominal attributes number of instances numeric attributes numeric prediction outcome outlook output overcast overfitting parameters particular perceptron performance petal length play possible pre-presbyopic probability problem produce pruning represent result rule set sample Section setosa simple split statistical structure subset subtree sunny support vector machines Table tear production rate techniques temperature test instance test set tion training data training instances training set true weather data weights Weka windy zero
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