Introduction To Data Mining 2nd Edition Pdf Free Download

We have the best and no restriction downloading facility for any book. In this site you can get into the introduction to data mining 2nd edition pdf free download. It is available for free here, and you can download it in a snap of your fingers. This has been possible through the efforts of a group of people whose only sense of duty is to ensure that people do not suffer by lack of reading materials.

Do you happen to have been searching for a place where you can download introduction to data mining 2nd edition pdf free download without restriction? Have you been searching for long without getting ant result? Then you just came to the end of your search as you need not search anymore. I bring you the latest information on this College Learners site where you can get introduction to data mining 2nd edition pdf free download without any cost or registration.

Introduction To Data Mining 2nd Edition Pdf Free Download >>>CLICK HERE<<< – This book is about the introduction to data mining. You can have a better understanding of this if you have the first edition of this book which was published in 2003 and now a new edition has been published in 2017. INTRODUCTION TO DATA MINING AND KNOWLEDGE DISCOVERY The data mining process involves four phases: 0 Exploratory data analysis (EDA)

What are you waiting for? All the PDF books you desire are now at your fingertips and accessible on this ebook site for free!

About Introduction To Data Mining 2nd Edition Pdf Free Download

Preface

1. What’s it all about? 1.1 Data mining and machine learning 1.2 Simple examples: the weather problem and others 1.3 Fielded applications 1.4 Machine learning and statistics 1.5 Generalization as search 1.6 Data mining and ethics 1.7 Further reading

2. Input: Concepts, instances, attributes 2.1 What’s a concept? 2.2 What’s in an example? 2.3 What’s in an attribute? 2.4 Preparing the input 2.5 Further reading

3. Output: Knowledge representation 3.1 Decision tables 3.2 Decision trees 3.3 Classification rules 3.4 Association rules 3.5 Rules with exceptions 3.6 Rules involving relations 3.7 Trees for numeric prediction 3.8 Instance-based representation 3.9 Clusters 3.10 Further reading

4. Algorithms: The basic methods 4.1 Inferring rudimentary rules 4.2 Statistical modeling 4.3 Divide-and-conquer: constructing decision trees 4.4 Covering algorithms: constructing rules 4.5 Mining association rules 4.6 Linear models 4.7 Instance-based learning 4.8 Clustering 4.9 Further reading

5. Credibility: Evaluating what’s been learned 5.1 Training and testing 5.2 Predicting performance 5.3 Cross-validation 5.4 Other estimates 5.5 Comparing data mining schemes 5.6 Predicting probabilities 5.7 Counting the cost 5.8 Evaluating numeric prediction 5.9 The minimum description length principle 5.10 Applying MDL to clustering 5.11 Further reading

6. Implementations: Real machine learning schemes 6.1 Decision trees 6.2 Classification rules 6.3 Extending linear models 6.4 Instance-based learning 6.5 Numeric prediction 6.6 Clustering 6.7 Bayesian networks

7. Transformations: Engineering the input and output 7.1 Attribute selection 7.2 Discretizing numeric attributes 7.3 Some useful transformations 7.4 Automatic data cleansing 7.5 Combining multiple models 7.6 Using unlabeled data 7.7 Further reading

8. Moving on: Extensions and applications 8.1 Learning from massive datasets 8.2 Incorporating domain knowledge 8.3 Text and Web mining 8.4 Adversarial situations 8.5 Ubiquitous data mining 8.6 Further reading

Part II: The Weka machine learning workbench

9. Introduction to Weka 9.1 What’s in Weka? 9.2 How do you use it? 9.3 What else can you do?

10. The Explorer 10.1 Getting started 10.2 Exploring the Explorer 10.3 Filtering algorithms 10.4 Learning algorithms 10.5 Meta-learning algorithms 10.6 Clustering algorithms 10.7 Association-rule learners 10.8 Attribute selection

11. The Knowledge Flow interface 11.1 Getting started 11.2 Knowledge Flow components 11.3 Configuring and connecting the components 11.4 Incremental learning

12. The Experimenter 12.1 Getting started 12.2 Simple setup 12.3 Advanced setup 12.4 The Analyze panel 12.5 Distributing processing over several machines

13. The command-line interface 13.1 Getting started 13.2 The structure of Weka 13.3 Command-line options

14. Embedded machine learning

15. Writing new learning schemes

Table of content of Introduction To Data Mining 2nd Edition Pdf Free Download

Preface

1. What’s it all about? 1.1 Data mining and machine learning 1.2 Simple examples: the weather problem and others 1.3 Fielded applications 1.4 Machine learning and statistics 1.5 Generalization as search 1.6 Data mining and ethics 1.7 Further reading

2. Input: Concepts, instances, attributes 2.1 What’s a concept? 2.2 What’s in an example? 2.3 What’s in an attribute? 2.4 Preparing the input 2.5 Further reading

3. Output: Knowledge representation 3.1 Decision tables 3.2 Decision trees 3.3 Classification rules 3.4 Association rules 3.5 Rules with exceptions 3.6 Rules involving relations 3.7 Trees for numeric prediction 3.8 Instance-based representation 3.9 Clusters 3.10 Further reading

4. Algorithms: The basic methods 4.1 Inferring rudimentary rules 4.2 Statistical modeling 4.3 Divide-and-conquer: constructing decision trees 4.4 Covering algorithms: constructing rules 4.5 Mining association rules 4.6 Linear models 4.7 Instance-based learning 4.8 Clustering 4.9 Further reading

5. Credibility: Evaluating what’s been learned 5.1 Training and testing 5.2 Predicting performance 5.3 Cross-validation 5.4 Other estimates 5.5 Comparing data mining schemes 5.6 Predicting probabilities 5.7 Counting the cost 5.8 Evaluating numeric prediction 5.9 The minimum description length principle 5.10 Applying MDL to clustering 5.11 Further reading

6. Implementations: Real machine learning schemes 6.1 Decision trees 6.2 Classification rules 6.3 Extending linear models 6.4 Instance-based learning 6.5 Numeric prediction 6.6 Clustering 6.7 Bayesian networks

7. Transformations: Engineering the input and output 7.1 Attribute selection 7.2 Discretizing numeric attributes 7.3 Some useful transformations 7.4 Automatic data cleansing 7.5 Combining multiple models 7.6 Using unlabeled data 7.7 Further reading

8. Moving on: Extensions and applications 8.1 Learning from massive datasets 8.2 Incorporating domain knowledge 8.3 Text and Web mining 8.4 Adversarial situations 8.5 Ubiquitous data mining 8.6 Further reading

Part II: The Weka machine learning workbench

9. Introduction to Weka 9.1 What’s in Weka? 9.2 How do you use it? 9.3 What else can you do?

10. The Explorer 10.1 Getting started 10.2 Exploring the Explorer 10.3 Filtering algorithms 10.4 Learning algorithms 10.5 Meta-learning algorithms 10.6 Clustering algorithms 10.7 Association-rule learners 10.8 Attribute selection

11. The Knowledge Flow interface 11.1 Getting started 11.2 Knowledge Flow components 11.3 Configuring and connecting the components 11.4 Incremental learning

12. The Experimenter 12.1 Getting started 12.2 Simple setup 12.3 Advanced setup 12.4 The Analyze panel 12.5 Distributing processing over several machines

13. The command-line interface 13.1 Getting started 13.2 The structure of Weka 13.3 Command-line options

14. Embedded machine learning

15. Writing new learning schemes

Leave a Comment