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Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks.
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
Item #: 104352132

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Item #: 104352132

XPF 10450

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Presents a comprehensive new chapter on deep learning, including improving training of deep learning models, convolutional neural networks, recurrent neural networks, and graph neural networks.
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What Stands Out

Comprehensive Coverage
This edition provides extensive insights into data mining techniques, ensuring a thorough understanding for both beginners and seasoned professionals, addressing core concepts with detailed explanations.
Practical Applications
Real-world examples illustrate data mining applications, helping readers connect theoretical knowledge to practical use cases, enhancing learning and retention of complex concepts.
Updated Content
Includes the latest advancements and methodologies in data mining, keeping the content relevant and reflective of current industry practices, making it an invaluable resource for modern data practitioners.

Product Details

Shop Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) online at a best price in Wallis and Futuna Islands. 0128117605
Publisher Morgan Kaufmann
Publication date October 17, 2022
Edition 4th
Language English
Print length 752 pages
ISBN-10 9780128117606
ISBN-13 978-0128117606
Item Weight 7.6 ounces (215.46 grams)
Dimensions 7.25 x 1.25 x 8.75 inches (18.4 x 3.2 x 22.2 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists seeking a comprehensive foundation in data mining methodologies and techniques for advanced analytics.

  • Academics and Researchers

    Useful for educators and researchers who require in-depth theoretical knowledge and practical applications in data mining.

  • Data Analysts

    Beneficial for data analysts looking to enhance their skills in extracting meaningful patterns from large datasets.

Not Suitable For
  • Beginners in Data

    Not suitable for users with little to no background in data mining or analytics, as it requires foundational knowledge.

Product Description

Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

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Expert Systems Editorial Review

**Editorial Review of "Data Mining: Concepts and Techniques" (4th Edition)** "Data Mining: Concepts and Techniques" emerges as a significant contribution to the field of data mining literature, illustrating various methodologies with a vendor-neutral approach that has resonated well with several professionals. Many reviewers appreciate the comprehensible presentation of mathematical foundations behind data mining concepts, making it accessible to readers who may not be well-versed in the intricacies of algorithms. The use of a Consistent fictitious organization throughout the book helps unify diverse topics and offers a pragmatic perspective to theoretical ideas. However, despite its strengths, critical feedback highlights several shortcomings that potential readers should Consider. While the book serves as a good introduction, many users noted that it lacks depth—expressing the need to Consult original research papers for a more comprehensive understanding of the subject matter. Users found the absence of answers to exercises and solutions manuals a significant drawback, reducing the book's utility as a self-study resource. Additionally, some reviewers critiqued the repeated concepts throughout different chapters, which can dilute the learning experience and create confusion rather than clarity. There remains a clear split in reader reception—those who appreciate its foundational approach and those who find it lacking in thoroughness. Despite these critiques, with a structured question-and-answer format, the book appeals to learners who prefer clear and organized introductions to new concepts. **

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Pros

  • Vendor-neutral perspective allows flexibility in implementation.
  • Clear explanations of the mathematics behind data mining techniques.
  • Cohesive use of a fictional organization presents concepts uniformly.
  • Strong introductory text that appeals to beginners and practitioners alike.

Cons

  • Lacks depth; many concepts require external research for a complete understanding.

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