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188240692
Data Mining: The Textbook (Computer Science)
86% of respondents would recommend this to a friend
Item #:
188240692
GYD 14712
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This is the most amazing and comprehensive text book on data mining. It is a great book for graduate students and researchers as well as practitioners.
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What Stands Out
Comprehensive Coverage
This textbook provides in-depth insight into data mining concepts, algorithms, and applications, making it essential for students and professionals looking to elevate their understanding of the domain.
Practical Examples
Includes real-world case studies and practical examples, allowing readers to see application and implementation of data mining techniques, boosting learning and enhancing user familiarity with the tools.
Updated Content
The 2015 edition encompasses the latest advancements and methodologies in data mining, ensuring users have access to current practices, trends, and technologies in this rapidly evolving field.
Product Details
- This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike! -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and TechnologyThis is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners. -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
| Publisher | Springer |
| Publication date | April 13, 2015 |
| Edition | 2015th |
| Language | English |
| File size | 23.4 MB |
| Screen Reader | Supported |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 764 pages |
| ISBN-13 | 978-3319141428 |
| Page Flip | Enabled |
| Item Weight | 1 lbs (450 grams) |
Who Should Buy?
-
Students
Ideal for university students studying data mining or related fields, providing foundational knowledge and comprehensive insights.
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Data Professionals
Beneficial for practitioners in data science and analytics looking to deepen their understanding of data mining techniques.
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Instructors
Useful for educators seeking a thorough textbook to guide their curriculum in data mining courses effectively.
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Beginners
Not suitable for absolute beginners without prior knowledge of programming or statistics in data mining.
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Probability & Statistics Editorial Review
Data Mining: The Textbook offers an in-depth exploration of various data mining algorithms and concepts, making it a valuable resource for both students and professionals. The book, published by Springer and weighing only 1 lb, features a comprehensive 764 pages filled with detailed algorithm descriptions and analyses. Readers appreciate the organized presentation of techniques and subcomponents, allowing for better understanding of data mining as a discipline. While it is noted that it may not serve as an introduction for novices, its coverage of topics such as outlier detection and association mining makes it a strong reference for advanced students and experienced practitioners alike.
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Pros
- Comprehensive coverage of data mining topics
- Great depth and breadth in descriptions
- Excellent references and taxonomy of techniques
- Suitable for academic and advanced readers
- Covers both data mining and machine learning concepts
Cons
- Not ideal for beginners or practical application
Product Price History
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GYD 14712
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Features & Benefits
- Comprehensive coverage of data mining topics, from fundamentals to advanced applications.
- Includes a wide variety of data types: text, time series, spatial, and more.
- Ideal for both introductory and advanced courses.
- Balances mathematical details with intuitive explanations.
- Packed with illustrations, examples, and exercises to enhance learning.
- Highly praised by experts, making it a must-have for students and educators.
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