Introduction to Data Mining (Second Edition) Link to Pearson Page of Book Provides both theoretical and practical coverage of all data mining topics. Introduction to Data Mining 1st Edition. Pang-Ning Tan (Author), Michael Steinbach (Author), Vipin Kumar (Author) & 0 more. Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data. Story time just got better with Prime Book Box, a subscription that delivers hand- picked children's books every 1, 2, or 3 months — at 40% off List Price.
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Overview Specifically, this book provides a comprehensive introduction to data mining and is designed to be accessible and useful to students. Introduction to Data Mining. Front Cover. Pang-Ning Tan. Pearson, 2 Reviews Bibliographic information. QR code for Introduction to Data Mining. Introduction to Data Mining Pearson Education, - Data mining - pages Want this. User Review - Flag as inappropriate. NIce book. All 6 reviews ».
The material on Bayesian networks, support vector machines, and artificial neural networks has been significantly expanded. We have added a separate section on deep networks to address the current developments in this area. The discussion of evaluation, which occurs in the section on imbalanced classes, has also been updated and improved.
Anomaly Detection: Anomaly detection has been greatly revised and expanded. The reconstruction-based approach is illustrated using autoencoder networks that are part of the deep learning paradigm.
Association Analysis: The changes in association analysis are more localized. We have completely reworked the section on the evaluation of association patterns introductory chapter , as well as the sections on sequence and graph mining advanced chapter.
Clustering: Changes to cluster analysis are also localized. The introductory chapter added the K-means initialization technique and an updated discussion of cluster evaluation.
The advanced clustering chapter adds a new section on spectral graph clustering. Data: The data chapter has been updated to include discussions of mutual information and kernel-based techniques.
Exploring Data: The data exploration chapter has been removed from the print edition of the book, but is available on the web. Teaching and Learning Experience This program will provide a better teaching and learning experience-for you and your students.
It will help: Present Fundamental Concepts and Algorithms: Written for the beginner, this text provides both theoretical and practical coverage of all data mining topics. Support Learning: Instructor resources include solutions for exercises and a complete set of lecture slides. Product details Format Paperback pages Dimensions x x Other books in this series.
Add to basket. Starting Out with Java Tony Gaddis. He received his M.
His research interests focus on the development of novel data mining algorithms for a broad range of applications, including climate and ecological sciences, cybersecurity, and network analysis. His research interests are in the areas of data mining, machine learning, and statistical learning and its applications to fields, such as climate, biology, and medicine.
Introduction to Data Mining (Second Edition)
This research has resulted in more than papers published in the proceedings of major data mining conferences or computer science or domain journals. Previous to his academic career, he held a variety of software engineering, analysis, and design positions in industry at Silicon Biology, Racotek, and NCR. Vipin Kumar.
His research interests lie in the development of data mining and machine learning algorithms for solving scientific and socially relevant problems in varied disciplines such as climate science, hydrology, and healthcare. Rating details. Book ratings by Goodreads. Goodreads is the world's largest site for readers with over 50 million reviews.
Top 5 Data Mining Books for Computer Scientists
We're featuring millions of their reader ratings on our book pages to help you find your new favourite book. Close X.Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time.
If you like books and love to build cool products, we may be looking for you. Basic Concepts and Algorithms 6. Instructor Resources.
It is also written by a top data mining researcher C.
We're featuring millions of their reader ratings on our book pages to help you find your new favourite book. Almost every section of the advanced classification chapter has been significantly updated.
Table of Contents
Includes extensive number of integrated examples and figures. Topics covered include classification, association analysis, clustering, anomaly detection, and avoiding false discoveries.
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