2000 DataMiningPracticalMLToolsWithJava

• (Witten & Frank, 2000) ⇒ Ian H. Witten, and Eibe Frank. (2000). "Data Mining: practical machine learning tools and techniques with Java implementations." Morgan Kaufmann. ISBN:1558605525

Subject Headings: Data Mining Text Book.

Notes

• A second edition of the book is (Witten & Frank, 2005)
• Used as reference for: Associated, Association Learning, Attribute Value, Attribute, Boolean Attribute, Categorical Attribute, Class Value, Classification Learning, Classification Learning, Classified, Closed World Assumption, Clustered, Clustering, Concept Description, Concept, Continuous Attribute, Database Mining, Denormalization, Dichotomous Attribute, Dichotomy, Discrete Attribute, Enumerated Attribute, Example, Feature, File Mining, Independent Instance, Instance, Integer-Valued Number, Interval Quantity, Learning Scheme, Learning Style, Machine Learning Scheme, Machine Learning System, Measurement Level, Missing Value, Nominal Quantity, Numeric Attribute, Numeric Prediction, Numeric Quantity, Numeric Value, Posthoc Analysis, Ratio Quantity, Real-Valued Number, Recursive Rule, Supervised Classification Learning, Supervised Learning, Supervised, Table Row.

Quotes

Book Overview

This book offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2000 DataMiningPracticalMLToolsWithJava	Ian H. Witten Eibe Frank			Data Mining: Practical Machine Learning Tools and Techniques with Java implementations			http://books.google.com/books/elsevier?id=6lVEKlrTq8EC			2000