2000 DataMiningPracticalMLToolsWithJava: Difference between revisions
m (Text replace - "==Notes ==" to "==Notes==") |
m (Text replace - "ms]] " to "m]]s ") |
||
| Line 57: | Line 57: | ||
Ordinal attributes are generally called ''numeric'', or perhaps ''continuous'', but without the implication of mathematical continuity. A special case of the nominal scale is the ''dichotomy'', which has only two members - often designates are ''true'' and ''false'', or ''yes'' and ''no'' in the weather data. Such attributes are sometimes called ''boolean''. | Ordinal attributes are generally called ''numeric'', or perhaps ''continuous'', but without the implication of mathematical continuity. A special case of the nominal scale is the ''dichotomy'', which has only two members - often designates are ''true'' and ''false'', or ''yes'' and ''no'' in the weather data. Such attributes are sometimes called ''boolean''. | ||
[[Machine Learning System|Machine learning | [[Machine Learning System|Machine learning system]]s can use a wide variety of other information about attributes. For instance, dimensional considerations could be used to restrict the search to expressions or comparisons that are dimensionally correct. Circular ordering could affect the kinds of tests that are considered. For example, in a temporal context, tests on a <code>day</code> attribute could involve <code>next day, previous day, next week, same day next week</code>. Partial orderings, that, generalize/specialization relations, frequently occur in practical situations. Information this kind is often referred to as ''metadata'', data about data. However, the kind of practical schemes currently used for data mining are rarely capable of taking metadata into account, although it is likely that these capabilities will develop rapidly in the future. | ||
====Missing values==== | ====Missing values==== | ||
Revision as of 01:31, 21 March 2014
- (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 |