Data Mining Practical Machine Learning Tools and Techniques
رقم التسجيلة | 3744 |
نوع المادة | book |
الموقع الالكتروني | http://tinyurl.com/3svzsvb |
ردمك | 9780120884070 |
رقم الطلب |
QA76.9.D343W58 |
المؤلف | Witten, Lan H |
العنوان | Data Mining Practical Machine Learning Tools and Techniques |
بيان الطبعة | 2nd. P |
بيانات النشر | San Francisco: Elsevier, 2005. |
الوصف المادي | 525. P |
بيان السلسلة | Morgan Kaufmann series in data management systems |
المحتويات / النص |
Preface 1. What’s it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating what’s been learned 6. Implementations: Real machine learning schemes 7. Transformations: Engineering the input and output 8. Moving on: Extensions and applications Part II: The Weka machine learning workbench 9. Introduction to Weka 10. The Explorer 11. The Knowledge Flow interface 12. The Experimenter 13. The command-line interface 14. Embedded machine learning 15. Writing new learning schemes References Index |
المستخلص |
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these 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 for the 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; plus much more. + Authors, Ian Witten and Eibe Frank, recipients of the 2005 ACM SIGKDD Service Award. + Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods; + Performance improvement techniques that work by transforming the input or output; + Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization—in a new, interactive interface. |
المواضيع | Data mining |
الأسماء المرتبطة | Frank, Eibe |
LDR | 00115cam a22002053a 4500 |
020 | |a 9780120884070 |
050 | |a QA76.9.D343W58 |
100 | |a Witten, Lan H |
245 | |a Data Mining Practical Machine Learning Tools and Techniques |
250 | |a 2nd. P |
260 | |a San Francisco |b Elsevier, |c 2005 |
300 | |a 525. P |
490 | |a Morgan Kaufmann series in data management systems |
505 | |a Preface 1. What’s it all about? 2. Input: Concepts, instances, attributes 3. Output: Knowledge representation 4. Algorithms: The basic methods 5. Credibility: Evaluating what’s been learned 6. Implementations: Real machine learning schemes 7. Transformations: Engineering the input and output 8. Moving on: Extensions and applications Part II: The Weka machine learning workbench 9. Introduction to Weka 10. The Explorer 11. The Knowledge Flow interface 12. The Experimenter 13. The command-line interface 14. Embedded machine learning 15. Writing new learning schemes References Index |
520 | |a As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these 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 for the 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; plus much more. + Authors, Ian Witten and Eibe Frank, recipients of the 2005 ACM SIGKDD Service Award. + Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods; + Performance improvement techniques that work by transforming the input or output; + Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization—in a new, interactive interface. |
650 | |a Data mining |
700 | |a Frank, Eibe |
856 | |u http://tinyurl.com/3svzsvb |
910 | |a libsys:recno,3744 |
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