000			07283cam a2200397 i 4500
999			_c50355 _d50355
001			19184960
003			CITU
005			20230925123943.0
008			160721s2017 mau b 001 0 eng
010			_a 2016948470
020			_a9780128042915
040			_aDLC _beng _erda _cDLC
042			_apcc
050	0	0	_aQA76.9.D343 _bW58 2017
082			_a006.312
100	1		_aWitten, I. H. (Ian H.) _eauthor.
245	0	0	_aData mining : _bpractical machine learning tools and techniques / _cIan H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal.
250			_aFourth Edition.
264		1	_aCambridge, MA; _aAmsterdam : _bMorgan Kaufmann, _c[2017]
264		4	_cc2017
300			_axxxii, 621 pages ; _c24 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aRev. edition of: Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. c2013.
504			_aIncludes bibliographical references (pages 573-601) and index.
505			_aTable of Contents Part I: Introduction to data mining Chapter 1. What?s it all about? Abstract 1.1 Data Mining and Machine Learning 1.2 Simple Examples: The Weather Problem and Others 1.3 Fielded Applications 1.4 The Data Mining Process 1.5 Machine Learning and Statistics 1.6 Generalization as Search 1.7 Data Mining and Ethics 1.8 Further Reading and Bibliographic Notes Chapter 2. Input: Concepts, instances, attributes Abstract 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 and Bibliographic Notes Chapter 3. Output: Knowledge representation Abstract 3.1 Tables 3.2 Linear Models 3.3 Trees 3.4 Rules 3.5 Instance-Based Representation 3.6 Clusters 3.7 Further Reading and Bibliographic Notes Chapter 4. Algorithms: The basic methods Abstracts 4.1 Inferring Rudimentary Rules 4.2 Simple Probabilistic 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 Multi-instance Learning 4.10 Further Reading and Bibliographic Notes 4.11 Weka Implementations Chapter 5. Credibility: Evaluating what?s been learned Abstract 5.1 Training and Testing 5.2 Predicting Performance 5.3 Cross-Validation 5.4 Other Estimates 5.5 Hyperparameter Selection 5.6 Comparing Data Mining Schemes 5.7 Predicting Probabilities 5.8 Counting the Cost 5.9 Evaluating Numeric Prediction 5.10 The MDL Principle 5.11 Applying the MDL Principle to Clustering 5.12 Using a Validation Set for Model Selection 5.13 Further Reading and Bibliographic Notes Part II: More advanced machine learning schemes Chapter 6. Trees and rules Abstract 6.1 Decision Trees 6.2 Classification Rules 6.3 Association Rules 6.4 Weka Implementations Chapter 7. Extending instance-based and linear models Abstract 7.1 Instance-Based Learning 7.2 Extending Linear Models 7.3 Numeric Prediction With Local Linear Models 7.4 Weka Implementations Chapter 8. Data transformations Abstracts 8.1 Attribute Selection 8.2 Discretizing Numeric Attributes 8.3 Projections 8.4 Sampling 8.5 Cleansing 8.6 Transforming Multiple Classes to Binary Ones 8.7 Calibrating Class Probabilities 8.8 Further Reading and Bibliographic Notes 8.9 Weka Implementations Chapter 9. Probabilistic methods Abstract 9.1 Foundations 9.2 Bayesian Networks 9.3 Clustering and Probability Density Estimation 9.4 Hidden Variable Models 9.5 Bayesian Estimation and Prediction 9.6 Graphical Models and Factor Graphs 9.7 Conditional Probability Models 9.8 Sequential and Temporal Models 9.9 Further Reading and Bibliographic Notes 9.10 Weka Implementations Chapter 10. Deep learning Abstract 10.1 Deep Feedforward Networks 10.2 Training and Evaluating Deep Networks 10.3 Convolutional Neural Networks 10.4 Autoencoders 10.5 Stochastic Deep Networks 10.6 Recurrent Neural Networks 10.7 Further Reading and Bibliographic Notes 10.8 Deep Learning Software and Network Implementations 10.9 WEKA Implementations Chapter 11. Beyond supervised and unsupervised learning Abstract 11.1 Semisupervised Learning 11.2 Multi-instance Learning 11.3 Further Reading and Bibliographic Notes 11.4 WEKA Implementations Chapter 12. Ensemble learning Abstract 12.1 Combining Multiple Models 12.2 Bagging 12.3 Randomization 12.4 Boosting 12.5 Additive Regression 12.6 Interpretable Ensembles 12.7 Stacking 12.8 Further Reading and Bibliographic Notes 12.9 WEKA Implementations Chapter 13. Moving on: applications and beyond Abstract 13.1 Applying Machine Learning 13.2 Learning From Massive Datasets 13.3 Data Stream Learning 13.4 Incorporating Domain Knowledge 13.5 Text Mining 13.6 Web Mining 13.7 Images and Speech 13.8 Adversarial Situations 13.9 Ubiquitous Data Mining 13.10 Further Reading and Bibliographic Notes 13.11 WEKA Implementations Appendix A. Theoretical foundations A.1 Matrix Algebra A.2 Fundamental Elements of Probabilistic Methods Appendix B. The WEKA workbench B.1 What?s in WEKA? B.2 The package management system B.3 The Explorer B.4 The Knowledge Flow Interface B.5 The Experimenter
520			_aDescription Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. View more > Key Features Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface Includes open-access online courses that introduce practical applications of the material in the book
650		0	_aData mining.
700	1		_aFrank, Eibe _eauthor
700	1		_aHall, Mark A. _eauthor
700	1		_aPal, Christopher J. _dauthor
906			_a7 _bcbc _corignew _d2 _eepcn _f20 _gy-gencatlg
942			_2ddc _cBK _02