Data Mining and Machine Learning

Data Mining and Machine Learning
PDF
  • eBook:
    Data Mining and Machine Learning: Fundamental Concepts and Algorithms, 2nd Edition
  • Author:
    Mohammed J. Zaki, Wagner Meira Jr
  • Edition:
    2 edition
  • Categories:
  • Data:
    March 12, 2020
  • ISBN:
    1108473989
  • ISBN-13:
    9781108473989
  • Language:
    English
  • Pages:
    776 pages
  • Format:
    PDF

Book Description
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.

Content

PART ONE: DATA ANALYSIS FOUNDATIONS
1. Data Matrix
2. Numeric Attributes
3. Categorical Attributes
4. Graph Data
5. Kernel Methods
6. High-dimensional Data
7. Dimensionality Reduction

PART TWO: FREQUENT PATTERN MINING
8. Itemset Mining
9. Summarizing Itemsets
10. Sequence Mining
11. Graph Pattern Mining
12. Pattern and Rule Assessment

PART THREE: CLUSTERING
13. Representative-based Clustering
14. Hierarchical Clustering
15. Density-based Clustering
16. Spectral and Graph Clustering
17. Clustering Validation

PART FOUR: CLASSIFICATION
18. Probabilistic Classification
19. Decision Tree Classifier
20. Linear Discriminant Analysis
21. Support Vector Machines
22. Classification Assessment

PART FIVE: REGRESSION
23. Linear Regression
24. Logistic Regression
25. Neural Networks
26. Deep Learning
27. Regression Evaluation

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