Java Data Mining
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. Topics include: Data mining and machine learning basics, sample datasets and applications for data mining, machine learning vs. statistics, the ethics of data mining, generalization, concepts, attributes, missing values, decision tables and trees, classification rules, association rules, exceptions, numeric prediction, clustering, algorithms and implementations in Java, inferring rules, statistical modeling, covering algorithms, linear models, support vector machines, instance-based learning, credibility, cross-validation, probability, costs (lift charts and ROC curves), selecting attributes, data cleansing, combining multiple models (bagging, boosting, and stacking), Weka (reusable Java classes for machine learning), customizing Weka, visualizing machine learning.
- Cost: $49.95
- Pages: 416
- Edition: 1st edition
- ISBN: 1558605525
Resource Specification
Category:
Title / Program Name:
Java Data Mining
URL:
http://www.amazon.com/exec/obidos/ASIN/1558605525/bigwebmasters-20
Screenshots URL:
http://www.bigwebmaster.com/screenshots/1558605525.jpg
Released Date:
October 11, 1999
Cost:
$49.95
List Price:
$49.95
Pages:
416
Edition:
1st edition
ISBN:
1558605525
Publisher:
Morgan Kaufmann
Author:
Ian H. Witten, Eibe Frank
Keywords:
java text mining, java machine learning
Summary:
Thorough grounding in machine learning concepts with Java
Description:
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. Topics include: Data mining and machine learning basics, sample datasets and applications for data mining, machine learning vs. statistics, the ethics of data mining, generalization, concepts, attributes, missing values, decision tables and trees, classification rules, association rules, exceptions, numeric prediction, clustering, algorithms and implementations in Java, inferring rules, statistical modeling, covering algorithms, linear models, support vector machines, instance-based learning, credibility, cross-validation, probability, costs (lift charts and ROC curves), selecting attributes, data cleansing, combining multiple models (bagging, boosting, and stacking), Weka (reusable Java classes for machine learning), customizing Weka, visualizing machine learning.
Ratings Breakdown
Number of Votes:
8
Resource Rating:
1.75
Highest Rating:
4
Lowest Rating:
1
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| 2 | 12% | 1 |
| 1 | 62% | 5 |
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