Data Mining Practical Machine Learning Tools and Techniques with Java Implementations

ISBN-10: 1558605525

ISBN-13: 9781558605527

Edition: 1999

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Description: This work offers a grounding in machine learning concepts combined with practical advice on applying machine learning tools and techniques in real-world data mining situations.

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Book details

List price: $60.95
Copyright year: 1999
Publisher: Elsevier Science & Technology Books
Publication date: 10/11/1999
Binding: Paperback
Pages: 371
Size: 7.25" wide x 9.25" long x 1.00" tall
Weight: 1.430
Language: English

Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.

Foreword
Preface
What's it all about?
Data mining and machine learning
Describing structural patterns
Machine learning
Data mining
Simple examples: The weather problem and others
The weather problem
Contact lenses: An idealized problem
Irises: A classic numeric dataset
CPU performance: Introducing numeric prediction
Labor negotiations: A more realistic example
Soybean classification: A classic machine learning success
Fielded applications
Decisions involving judgment
Screening images
Load forecasting
Diagnosis
Marketing and sales
Machine learning and statistics
Generalization as search
Enumerating the concept space
Bias
Data mining and ethics
Further reading
Input: Concepts, instances, attributes
What's a concept?
What's in an example?
What's in an attribute?
Preparing the input
Gathering the data together
Arff format
Attribute types
Missing values
Inaccurate values
Getting to know your data
Further reading
Output: Knowledge representation
Decision tables
Decision trees
Classification rules
Association rules
Rules with exceptions
Rules involving relations
Trees for numeric prediction
Instance-based representation
Clusters
Further reading
Algorithms: The basic methods
Inferring rudimentary rules
Missing values and numeric attributes
Discussion
Statistical modeling
Missing values and numeric attributes
Discussion
Divide and conquer: Constructing decision trees
Calculating information
Highly branching attributes
Discussion
Covering algorithms: Constructing rules
Rules versus trees
A simple covering algorithm
Rules versus decision lists
Mining association rules
Item sets
Association rules
Generating rules efficiently
Discussion
Linear models
Numeric prediction
Classification
Discussion
Instance-based learning
The distance function
Discussion
Further reading
Credibility: Evaluating what's been learned
Training and testing
Predicting performance
Cross-validation
Other estimates
Leave-one-out
The bootstrap
Comparing data mining schemes
Predicting probabilities
Quadratic loss function
Informational loss function
Discussion
Counting the cost
Lift charts
ROC curves
Cost-sensitive learning
Discussion
Evaluating numeric prediction
The minimum description length principle
Applying MDL to clustering
Further reading
Implementations: Real machine learning schemes
Decision trees
Numeric attributes
Missing values
Pruning
Estimating error rates
Complexity of decision tree induction
From trees to rules
C4.5: Choices and options
Discussion
Classification rules
Criteria for choosing tests
Missing values, numeric attributes
Good rules and bad rules
Generating good rules
Generating good decision lists
Probability measure for rule evaluation
Evaluating rules using a test set
Obtaining rules from partial decision trees
Rules with exceptions
Discussion
Extending linear classification: Support vector machines
The maximum margin hyperplane
Nonlinear class boundaries
Discussion
Instance-based learning
Reducing the number of exemplars
Pruning noisy exemplars
Weighting attributes
Generalizing exemplars
Distance functions for generalized exemplars
Generalized distance functions
Discussion
Numeric prediction
Model trees
Building the tree
Pruning the tree
Nominal attributes
Missing values
Pseudo-code for model tree induction
Locally weighted linear regression
Discussion
Clustering
Iterative distance-based clustering
Incremental clustering
Category utility
Probability-based clustering
The EM algorithm
Extending the mixture model
Bayesian clustering
Discussion
Moving on: Engineering the input and output
Attribute selection
Scheme-independent selection
Searching the attribute space
Scheme-specific selection
Discretizing numeric attributes
Unsupervised discretization
Entropy-based discretization
Other discretization methods
Entropy-based versus error-based discretization
Converting discrete to numeric attributes
Automatic data cleansing
Improving decision trees
Robust regression
Detecting anomalies
Combining multiple models
Bagging
Boosting
Stacking
Error-correcting output codes
Further reading
Nuts and bolts: Machine learning algorithms in Java
Getting started
Javadoc and the class library
Classes, instances, and packages
The weka.core package
The weka.classifiers package
Other packages
Indexes
Processing datasets using the machine learning programs
Using M5'
Generic options
Scheme-specific options
Classifiers
Meta-learning shemes
Filters
Association rules
Clustering
Embedded machine learning
A simple message classifier
Writing new learning schemes
An example classifier
Conventions for implementing classifiers
Writing filters
An example filter
Conventions for writing filters
Looking forward
Learning from massive datasets
Visualizing machine learning
Visualizing the input
Visualizing the output
Incorporating domain knowledge
Text mining
Finding key phrases for documents
Finding information in running text
Soft parsing
Mining the World Wide Web
Further reading
References
Index
About the authors
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