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Data Mining: Concepts and Techniques 4/e

作者：Jiawei Han, Jian Pei, Hanghang Tong
原價：NT$ 3,050

ISBN：9780128117606
版次：4
年份：2022
出版商：Elsevier B.V.
頁數/規格：784頁/精裝單色
參考網頁：Data Mining: Concepts and Techniques 4/e

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Description
Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets.

After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.

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Key features

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

Addresses advanced topics in one dedicated chapter: data mining trends and research frontiers, including mining rich data types (text, spatiotemporal data, and graph/networks), data mining applications (such as sentiment analysis, truth discovery, and information propagattion), data mining methodologie and systems, and data mining and society

Provides a comprehensive, practical look at the concepts and techniques needed to get the most out of your data

Visit the author-hosted companion site, https://hanj.cs.illinois.edu/bk4/ for downloadable lecture slides and errata

Table of contents
Chapter 1: Introduction
Chapter 2: Data, measurements, and data preprocessing
Chapter 3: Data warehousing and online analytical processing
Chapter 4: Pattern mining: basic concepts and methods
Chapter 5: Pattern mining: advanced methods
Chapter 6: Classification: basic concepts and methods
Chapter 7: Classification: advanced methods
Chapter 8: Cluster analysis: basic concepts and methods
Chapter 9: Cluster analysis: advanced methods
Chapter 10: Deep learning
Chapter 11: Outlier detection
Chapter 12: Data mining trends and research frontiers

Appendix A: Mathematical background
Bibliography
Bibliography
Bibliography
Index

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