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Data Mining: The Textbook (Computer Science)
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Data Mining: The Textbook (Computer Science)
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.
Data Mining: The Textbook (Computer Science)
Item #: 188240692

Data Mining: The Textbook (Computer Science)

86% of respondents would recommend this to a friend

Item #: 188240692

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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

Shop Data Mining: The Textbook (Computer Science) online at a best price in Namibia. B00W3CKT2C
  • 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 Weight1 lbs (450 grams)

Who Should Buy?

Suitable For
  • Students

    Ideal for university students studying data mining or related fields, providing foundational knowledge and comprehensive insights.

  • Data Professionals

    Beneficial for practitioners in data science and analytics looking to deepen their understanding of data mining techniques.

  • Instructors

    Useful for educators seeking a thorough textbook to guide their curriculum in data mining courses effectively.

Not Suitable For
  • Beginners

    Not suitable for absolute beginners without prior knowledge of programming or statistics in data mining.

Product Description

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Customer Questions & Answers

  • Question: What topics does 'Data Mining: The Textbook' cover?

    Answer: The textbook covers a comprehensive range of topics related to data mining, including basic concepts, algorithms, and applications. It delves into core techniques such as classification, clustering, and association rule mining, while also exploring newer trends like deep learning and big data analytics. This broad spectrum ensures that both beginners and advanced learners can grasp the foundational principles and intricate methodologies of data mining, making it suitable for students, researchers, and professionals in the field.
  • Question: Who is the intended audience for 'Data Mining: The Textbook'?

    Answer: 'Data Mining: The Textbook' is aimed at students, educators, and professionals involved in data science and analytics. It serves as an essential resource for undergraduate and graduate courses, as well as a reference guide for practitioners looking to enhance their skills. The content is structured to cater to varying levels of expertise, enabling readers from diverse backgrounds to deepen their understanding of data mining principles and applications.
  • Question: How is 'Data Mining: The Textbook' structured?

    Answer: The textbook is organized in a logical manner, starting with fundamental concepts before progressing to complex methodologies. Each chapter includes theoretical explanations, practical examples, and case studies to reinforce learning. This structured approach allows for seamless comprehension, making it effective for study sessions or quick reference. Readers can easily follow the progression from basic to advanced topics, facilitating a thorough grasp of data mining techniques.
  • Question: What makes 'Data Mining: The Textbook' different from other books on the subject?

    Answer: 'Data Mining: The Textbook' distinguishes itself through its detailed explanations and emphasis on practical applications. While many books focus solely on theoretical aspects, this textbook integrates real-world examples that illustrate how data mining techniques can be applied in industries such as healthcare, finance, and marketing. This blend of theory and practice makes it a valuable resource for those looking to implement data mining in various scenarios.
  • Question: Are there any supplementary materials available with 'Data Mining: The Textbook'?

    Answer: Yes, 'Data Mining: The Textbook' often comes with supplementary materials such as online resources, datasets, and software tools that facilitate hands-on learning. These additional resources enhance the reading experience by providing practical exercises and examples. Students and educators can leverage these tools for projects and assignments, ensuring a more interactive learning process that solidifies understanding of data mining techniques.
  • Question: What prior knowledge is recommended before reading 'Data Mining: The Textbook'?

    Answer: A foundational understanding of statistics and programming is recommended before diving into 'Data Mining: The Textbook.' Basic knowledge in these areas will help readers grasp the algorithms and techniques discussed in the book more effectively. Familiarity with programming languages like Python or R can be particularly beneficial, as these skills allow readers to apply the concepts to real data sets and gain practical experience.
  • Question: Is 'Data Mining: The Textbook' suitable for self-study?

    Answer: Yes, 'Data Mining: The Textbook' is highly suitable for self-study. Its well-structured chapters, clear explanations, and practical examples make it accessible for independent learners. Individuals can progress at their own pace, utilizing the exercises and case studies to reinforce their understanding. This makes it an ideal resource for anyone looking to improve their data mining skills outside of a formal educational setting.
  • Question: How can 'Data Mining: The Textbook' be used in a professional context?

    Answer: In a professional context, 'Data Mining: The Textbook' can be utilized to train teams in data analytics techniques, improve business decision-making, and develop data-driven strategies. Organizations can use the methodologies outlined in the book to analyze customer data, optimize marketing campaigns, and enhance operational efficiency. By applying the book's techniques, professionals can turn data insights into actionable business solutions.
  • Question: Can 'Data Mining: The Textbook' help with exam preparation?

    Answer: 'Data Mining: The Textbook' is an excellent resource for exam preparation. Its detailed explanations, summary sections, and practice questions can help reinforce knowledge and identify areas needing further study. Students can use the textbook to review key concepts and practice problem-solving, ensuring they are well-prepared for exams in data mining and related courses.
  • Question: Where can I buy 'Data Mining: The Textbook' in Namibia?

    Answer: You can purchase 'Data Mining: The Textbook' from Ubuy, which offers a wide selection of educational materials. Ubuy provides a convenient platform to find this textbook, allowing you to browse various editions and formats. With user-friendly navigation and detailed product descriptions, Ubuy ensures you can easily locate and acquire the book to support your data mining studies.

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.

Customer Reviews & Ratings

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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

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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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