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Data Mining and Analysis Fundamental Concepts and Algorithms 1st edition by Mohammed Zaki, Wagner Meira ISBN 0521766333 9780521766333

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Data Mining and Analysis Fundamental Concepts and Algorithms 1st edition by Mohammed Zaki, Wagner Meira – Ebook PDF Instant Download/Delivery: 0521766333, 9780521766333
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ISBN 10: 0521766333
ISBN 13: 9780521766333
Author: Mohammed Zaki, Wagner Meira

The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics. This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike. Key features: • Covers both core methods and cutting-edge research • Algorithmic approach with open-source implementations • Minimal prerequisites: all key mathematical concepts are presented, as is the intuition behind the formulas • Short, self-contained chapters with class-tested examples and exercises allow for flexibility in designing a course and for easy reference • Supplementary website with lecture slides, videos, project ideas, and more

Data Mining and Analysis Fundamental Concepts and Algorithms 1st Table of contents:

Part I: Data Analysis Foundations

  1. Data Mining and Analysis

  2. Numeric Attributes

  3. Categorical Attributes

  4. Graph Data

  5. Kernel Methods

  6. High-Dimensional Data

  7. Dimensionality Reduction

Part II: Frequent Pattern Mining

  1. Itemset Mining

  2. Summarizing Itemsets

  3. Sequence Mining

  4. Graph Pattern Mining

  5. Pattern and Rule Assessment

Part III: Clustering

  1. Representative-Based Clustering

  2. Hierarchical Clustering

  3. Density-Based Clustering

  4. Spectral and Graph Clustering

  5. Clustering Validation

Part IV: Classification

  1. Probabilistic Classification

  2. Decision Tree Classifier

  3. Linear Discriminant Analysis

  4. Support Vector Machines

  5. Classification Assessment

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Tags: Mohammed Zaki, Wagner Meira, Data Mining, Analysis Fundamental