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Data mining and machine learning : fundamental concepts and algorithms Mohammed J Zaki; Wagner Meira

By: Material type: TextTextPublication details: NY : Cambridge University Press, 2020. ©2020Edition: 2NDDescription: 766 pagesISBN:
  • 9781108473989
DDC classification:
  • 006.312 ZAK
Contents:
Part one: Data analysis foundations Part two: Frequent pattern mining - Part three: Clustering Part four: Classification Part five: Regression
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
List(s) this item appears in: New Arrivals Oct-Nov 2021 - Central Library IIITDMK
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Item type Current library Collection Call number Status Date due Barcode
Books Books IIITDM Kurnool General Stacks Non-fiction 006.312 ZAK (Browse shelf(Opens below)) Checked out 29.12.2025 0003793
Reference Reference IIITDM Kurnool Reference Reference 006.312 ZAK (Browse shelf(Opens below)) Not For Loan 0003794

Part one: Data analysis foundations Part two: Frequent pattern mining - Part three: Clustering Part four: Classification Part five: Regression

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

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