Data mining and machine learning : fundamental concepts and algorithms / Mohammed J. Zaki, Wagner Meira, Jr.

Format:

Book

Author/Creator:

Zaki, Mohammed J., 1971- author.

Meira, Wagner, 1967- author.

Standardized Title:

Data mining and analysis

Language:

English

Subjects (All):

Data mining.

Physical Description:

xii, 766 pages : illustrations ; 26 cm

Edition:

Second edition.

Place of Publication:

Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2020.

Summary:

"The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts.New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning"-- Provided by publisher.

Contents:

Machine generated contents note: pt. ONE DATA ANALYSIS FOUNDATIONS

1. Data Matrix

1.1. Data Matrix

1.2. Attributes

1.3. Data: Algebraic and Geometric View

1.4. Data: Probabilistic View

1.5. Further Reading

1.6. Exercises

2. Numeric Attributes

2.1. Univariate Analysis

2.2. Bivariate Analysis

2.3. Multivariate Analysis

2.4. Data Normalization

2.5. Normal Distribution

2.6. Further Reading

2.7. Exercises

3. Categorical Attributes

3.1. Univariate Analysis

3.2. Bivariate Analysis

3.3. Multivariate Analysis

3.4. Distance and Angle

3.5. Discretization

3.6. Further Reading

3.7. Exercises

4. Graph Data

4.1. Graph Concepts

4.2. Topological Attributes

4.3. Centrality Analysis

4.4. Graph Models

4.5. Further Reading

4.6. Exercises

5. Kernel Methods

5.1. Kernel Matrix

5.2. Vector Kernels

5.3. Basic Kernel Operations in Feature Space

5.4. Kernels for Complex Objects

5.5. Further Reading

5.6. Exercises

6. High-dimensional Data

6.1. High-dimensional Objects

6.2. High-dimensional Volumes

6.3. Hypersphere Inscribed within Hypercube

6.4. Volume of Thin Hypersphere Shell

6.5. Diagonals in Hyperspace

6.6. Density of the Multivariate Normal

6.7. Appendix: Derivation of Hypersphere Volume

6.8. Further Reading

6.9. Exercises

7. Dimensionality Reduction

7.1. Background

7.2. Principal Component Analysis

7.3. Kernel Principal Component Analysis

7.4. Singular Value Decomposition

7.5. Further Reading

7.6. Exercises

pt. TWO FREQUENT PATTERN MINING

8. Itemset Mining

8.1. Frequent Itemsets and Association Rules

8.2. Itemset Mining Algorithms

8.3. Generating Association Rules

8.4. Further Reading

8.5. Exercises

9. Summarizing Itemsets

9.1. Maximal and Closed Frequent Itemsets

9.2. Mining Maximal Frequent Itemsets: GenMax Algorithm

9.3. Mining Closed Frequent Itemsets: Charm Algorithm

9.4. Nonderivable Itemsets

9.5. Further Reading

9.6. Exercises

10. Sequence Mining

10.1. Frequent Sequences

10.2. Mining Frequent Sequences

10.3. Substring Mining via Suffix Trees

10.4. Further Reading

10.5. Exercises

11. Graph Pattern Mining

11.1. Isomorphism and Support

11.2. Candidate Generation

11.3. The gSpan Algorithm

11.4. Further Reading

11.5. Exercises

12. Pattern and Rule Assessment

12.1. Rule and Pattern Assessment Measures

12.2. Significance Testing and Confidence Intervals

12.3. Further Reading

12.4. Exercises

pt. THREE CLUSTERING

13. Representative-based Clustering

13.1. K-means Algorithm

13.2. Kernel K-means

13.3. Expectation-Maximization Clustering

13.4. Further Reading

13.5. Exercises

14. Hierarchical Clustering

14.1. Preliminaries

14.2. Agglomerative Hierarchical Clustering

14.3. Further Reading

14.4. Exercises

15. Density-based Clustering

15.1. The DBSCAN Algorithm

15.2. Kernel Density Estimation

15.3. Density-based Clustering: DENCLUE

15.4. Further Reading

15.5. Exercises

16. Spectral and Graph Clustering

16.1. Graphs and Matrices

16.2. Clustering as Graph Cuts

16.3. Markov Clustering

16.4. Further Reading

16.5. Exercises

17. Clustering Validation

17.1. External Measures

17.2. Internal Measures

17.3. Relative Measures

17.4. Further Reading

17.5. Exercises

pt. FOUR CLASSIFICATION

18. Probabilistic Classification

18.1. Bayes Classifier

18.2. Naive Bayes Classifier

18.3. K Nearest Neighbors Classifier

18.4. Further Reading

18.5. Exercises

19. Decision Tree Classifier

19.1. Decision Trees

19.2. Decision Tree Algorithm

19.3. Further Reading

19.4. Exercises

20. Linear Discriminant Analysis

20.1. Optimal Linear Discriminant

20.2. Kernel Discriminant Analysis

20.3. Further Reading

20.4. Exercises

21. Support Vector Machines

21.1. Support Vectors and Margins

21.2. SVM: Linear and Separable Case

21.3. Soft Margin SVM: Linear and Nonseparable Case

21.4. Kernel SVM: Nonlinear Case

21.5. SVM Training: Stochastic Gradient Ascent

21.6. Further Reading

21.7. Exercises

22. Classification Assessment

22.1. Classification Performance Measures

22.2. Classifier Evaluation

22.3. Bias-Variance Decomposition

22.4. Ensemble Classifiers

22.5. Further Reading

22.6. Exercises

pt. FIVE REGRESSION

23. Linear Regression

23.1. Linear Regression Model

23.2. Bivariate Regression

23.3. Multiple Regression

23.4. Ridge Regression

23.5. Kernel Regression

23.6. L1 Regression: Lasso

23.7. Further Reading

23.8. Exercises

24. Logistic Regression

24.1. Binary Logistic Regression

24.2. Multiclass Logistic Regression

24.3. Further Reading

24.4. Exercises

25. Neural Networks

25.1. Artificial Neuron: Activation Functions

25.2. Neural Networks: Regression and Classification

25.3. Multilayer Perceptron: One Hidden Layer

25.4. Deep Multilayer Perceptrons

25.5. Further Reading

25.6. Exercises

26. Deep Learning

26.1. Recurrent Neural Networks

26.2. Gated RNNs: Long Short-Term Memory Networks

26.3. Convolutional Neural Networks

26.4. Regularization

26.5. Further Reading

26.6. Exercises

27. Regression Evaluation

27.1. Univariate Regression

27.2. Multiple Regression

27.3. Further Reading

27.4. Exercises.

Notes:

[Revised] edition of: Data mining and analysis. 2014.

Includes bibliographical references and index.

Other Format:

Online version: Zaki, Mohammed J., 1971- Data mining and machine learning

ISBN:

9781108473989

1108473989

OCLC:

1105725163

Publisher Number:

99987420834