Micheline Kamber Data Mining

ISBN 13 : 9789380931913

Data Mining

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9789380931913: Data Mining

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, itâ€tms still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous editionâ€tms publication, great advances have been made in the field of data mining. Not only does the third of edition of data mining: concepts and techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply todayâ€tms most powerful data mining techniques to meet real business challenges.

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Jiawei Han , Micheline Kamber , Jian Pei
ISBN 10 : 9380931913 ISBN 13 : 9789380931913
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Description du livre État : New. Brand new book. This is an international edition textbook with identical content as the US version. We ship all our orders from CA/IL, USA (depending on your address) and NOT from Asia! Choose expedited shipping for superfast delivery with tracking. N° de réf. du libraire 9789380931913

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Han, Jiawei
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Description du livre État : Brand New. Softcover, International Edition. This is an International Edition. N° de réf. du libraire 42714929A

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Han, Jiawei
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Description du livre Elsevier Science & Technology Books. État : Brand New. Softcover, International Edition. This is an International Edition. N° de réf. du libraire 43678386A

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Micheline Kamber, Jiawei Han & Jain Pei
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Description du livre Softcover. État : New. 3rd edition. Brand NEW, Paperback International Edition. Black & White or color, Cover and ISBN may be different but similar contents as US editions. Standard delivery takes 5-9 business days by USPS with tracking number. Choose expedited shipping for superfast delivery 2-4 business days by DHL/FEDEX. We also ship to PO Box addresses but by Standard delivery. International Edition Textbooks may bear a label -Not for sale in the U.S. or Canada- etc. printed only to discourage U.S. students from obtaining an affordable copy. Legal to use despite any disclaimer on cover as per US court. No access code or CD included unless specified. In some instances, the international textbooks may have different exercises at the end of the chapters. Printed in English. We may ship the books from multiple warehouses across the globe, including India depending upon the availability of inventory storage. 100% Customer satisfaction guaranteed! Please feel free to contact us for any queries. N° de réf. du libraire LPBDIN41486768

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Description du livre 2007. Soft cover. État : New. International Edition. Book Condition: BRAND NEW. International/Eastern Economy Edition, Paperback/Softcover with SAME TITLE, AUTHOR AND EDITION as listed. ISBN and Cover design differs. **100% IDENTICAL CONTENTS as U.S Edition**. Standard Delivery within 7-14 business days ACROSS THE GLOBE. We can ship to PO Box, APO address in US. International Edition Textbooks may bear a label (Not for sale in the U.S. or Canada) or (For sale in Asia only) or similar restrictions- printed only to discourage students from obtaining an affordable copy. US Court has asserted your right to buy and use International edition. Access code/CD may not provided with these editions. We may ship the books from MULTIPLE WAREHOUSES ACROSS THE GLOBE including Asia depending upon the availability of inventory. Printed in English. Customer satisfaction guaranteed. N° de réf. du libraire ABECA2387

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Kamber, Micheline
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Description du livre TBS. PAPERBACK. État : New. 9380931913 We ship from India. PAPERBACK INTERNATIONAL EDITION Brand New Copy. The ISBN-13 or Cover might be different but content is extactly same. We deliver in 5 - 9 days and actively resolve customer issues. N° de réf. du libraire 9380931913-ABA

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Jian Pei,Jiawei Han,Micheline Kamber
Edité par Elsevier (2017)
ISBN 10 : 9380931913 ISBN 13 : 9789380931913
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Description du livre Elsevier, 2017. Softcover. État : New. 5th or later edition. The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it?s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition?s publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today?s most powerful data mining techniques to meet real business challenges. Contents:- Chapter 1. Introduction 1 What Motivated Data Mining? Why Is It Important? 2 So, What Is Data Mining? 3 Data Mining--On What Kind of Data? 4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined? 5 Are All of the Patterns Interesting? 6 Classification of Data Mining Systems 7 Data Mining Task Primitives 8 Integration of a Data Mining System with a Database or Data Warehouse System 9 Major Issues in Data Mining 10 Summary Exercises Bibliographic Notes Chapter 2. Getting to Know Your Data 1. Types of Data Sets and Attribute Values 2. Basic Statistical Descriptions of Data 3. Data Visualization 4. Measuring Data Similarity 5. Summary Exercises Bibliographic Notes Chapter 3. Preprocessing 1. Data Quality 2. Major Tasks in Data Preprocessing 3. Data Reduction 4. Data Transformation and Data Discretization 5. Data Cleaning and Data Integration 6. Summary Exercises Bibliographic Notes Chapter 4. Data Warehousing and On-Line Analytical Processing 1. Data Warehouse: Basic Concepts 2. Data Warehouse Modeling: Data Cube and OLAP 3. Data Warehouse Design and Usage 4. Data Warehouse Implementation 5. Data Generalization by Attribute-Oriented Induction 6. Summary Exercises Bibliographic Notes Chapter 5. Data Cube Technology 1. Efficient Methods for Data Cube Computation 2. Exploration and Discovery in Multidimensional Databases 3. Summary Exercises Bibliographic Notes Chapter 6. Mining Frequent Patterns, Associations and Correlations: Concepts and Methods 1. Basic Concepts 2. E±cient and Scalable Frequent Itemset Mining Methods 3. Are All the Pattern Interesting?|Pattern Evaluation Methods 4. Applications of frequent pattern and associations 5. Summary Exercises Chapter 7. Advanced Frequent Pattern Mining 1. Frequent Pattern and Association Mining: A Road Map 2. Mining Various Kinds of Association Rules 3. Constraint-Based Frequent Pattern Mining 4. Extended Applications of Frequent Patterns 5. Summary Exercises Bibliographic Notes Chapter 8. Classification: Basic Concepts 1. Classification: Basic Concepts 2. Decision Tree Induction 3. Bayes Classi cation Methods 4. Rule-Based Classi cation 5. Model Evaluation and Selection 6. Techniques to Improve Classi cation Accuracy: Ensemble Methods 7. Handling Di®erent Kinds of Cases in Classi cation 8. Summary Exercises Bibliographic Notes Chapter 9. Classification: Advanced Methods 1. Bayesian Belief Networks 2. Classi cation by Neural Networks 3. Support Vector Machines 4. Pattern-Based Classi cation 5. Lazy Learners (or Learning from Your Neighbors) 6. Other Classi cation Methods 7. Summary E Printed Pages: 738. N° de réf. du libraire 40414

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8.

Jian Pei,Jiawei Han,Micheline Kamber
Edité par Elsevier (2017)
ISBN 10 : 9380931913 ISBN 13 : 9789380931913
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A - Z Books
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Description du livre Elsevier, 2017. Softcover. État : New. 5th or later edition. The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it’s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition’s publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today’s most powerful data mining techniques to meet real business challenges. Contents:- Chapter 1. Introduction 1 What Motivated Data Mining? Why Is It Important? 2 So, What Is Data Mining? 3 Data Mining--On What Kind of Data? 4 Data Mining Functionalities-What Kinds of Patterns Can Be Mined? 5 Are All of the Patterns Interesting? 6 Classification of Data Mining Systems 7 Data Mining Task Primitives 8 Integration of a Data Mining System with a Database or Data Warehouse System 9 Major Issues in Data Mining 10 Summary Exercises Bibliographic Notes Chapter 2. Getting to Know Your Data 1. Types of Data Sets and Attribute Values 2. Basic Statistical Descriptions of Data 3. Data Visualization 4. Measuring Data Similarity 5. Summary Exercises Bibliographic Notes Chapter 3. Preprocessing 1. Data Quality 2. Major Tasks in Data Preprocessing 3. Data Reduction 4. Data Transformation and Data Discretization 5. Data Cleaning and Data Integration 6. Summary Exercises Bibliographic Notes Chapter 4. Data Warehousing and On-Line Analytical Processing 1. Data Warehouse: Basic Concepts 2. Data Warehouse Modeling: Data Cube and OLAP 3. Data Warehouse Design and Usage 4. Data Warehouse Implementation 5. Data Generalization by Attribute-Oriented Induction 6. Summary Exercises Bibliographic Notes Chapter 5. Data Cube Technology 1. Efficient Methods for Data Cube Computation 2. Exploration and Discovery in Multidimensional Databases 3. Summary Exercises Bibliographic Notes Chapter 6. Mining Frequent Patterns, Associations and Correlations: Concepts and Methods 1. Basic Concepts 2. E±cient and Scalable Frequent Itemset Mining Methods 3. Are All the Pattern Interesting?|Pattern Evaluation Methods 4. Applications of frequent pattern and associations 5. Summary Exercises Chapter 7. Advanced Frequent Pattern Mining 1. Frequent Pattern and Association Mining: A Road Map 2. Mining Various Kinds of Association Rules 3. Constraint-Based Frequent Pattern Mining 4. Extended Applications of Frequent Patterns 5. Summary Exercises Bibliographic Notes Chapter 8. Classification: Basic Concepts 1. Classification: Basic Concepts 2. Decision Tree Induction 3. Bayes Classi cation Methods 4. Rule-Based Classi cation 5. Model Evaluation and Selection 6. Techniques to Improve Classi cation Accuracy: Ensemble Methods 7. Handling Di®erent Kinds of Cases in Classi cation 8. Summary Exercises Bibliographic Notes Chapter 9. Classification: Advanced Methods 1. Bayesian Belief Networks 2. Classi cation by Neural Networks 3. Support Vector Machines 4. Pattern-Based Classi cation 5. Lazy Learners (or Learning from Your Neighbors) 6. Other Classi cation Methods 7. Summary E Printed Pages: 738. N° de réf. du libraire 40414

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