Data Mining and Analysis
Fundamental Concepts and Algorithms
Nonfiction, Computers, Database Management, General Computing
| Author: | Mohammed J. Zaki, Wagner Meira, Jr | ISBN: | 9781107779105 |
| Publisher: | Cambridge University Press | Publication: | May 12, 2014 |
| Imprint: | Cambridge University Press | Language: | English |
| Author: | Mohammed J. Zaki, Wagner Meira, Jr |
| ISBN: | 9781107779105 |
| Publisher: | Cambridge University Press |
| Publication: | May 12, 2014 |
| Imprint: | Cambridge University Press |
| Language: | English |
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.
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.
