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104352132
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
90% of respondents would recommend this to a friend
Nº de artículo:
104352132
PYG 693800
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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.
Detalles de producto
| 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?
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Data Scientists
Ideal for data scientists seeking a comprehensive foundation in data mining methodologies and techniques for advanced analytics.
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Academics and Researchers
Useful for educators and researchers who require in-depth theoretical knowledge and practical applications in data mining.
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Data Analysts
Beneficial for data analysts looking to enhance their skills in extracting meaningful patterns from large datasets.
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Beginners in Data
Not suitable for users with little to no background in data mining or analytics, as it requires foundational knowledge.
DESCRIPCIÓN DEL PRODUCTO
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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ventajas
- 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.
Contras
- Lacks depth; many concepts require external research for a complete understanding.
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características y beneficios
- Learn core concepts and techniques in data mining.
- Understand knowledge discovery from massive data collections.
- Explore deep learning with dedicated chapter coverage.
- Access advanced topics on data mining trends and research frontiers.
- Practical application insights for mining various data types.
- Visit author’s companion site for valuable resources.
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