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【原文書】 書名：Data Mining: Practical Machine Learning Tools and Techniques 4/e 作者：Witten 出版社：Morgan Kaufmann 出版日期：2016/11/17 ISBN：9780128042915
| 書名: | Data Mining (4版) | |||
| 作者: | Witten | |||
| 版次: | 4 | |||
| ISBN: | 9780128042915 | |||
| 出版社: | Morgan Kaufmann (MK) | |||
| 出版日期: | 2016/12 | |||
| 書籍開數、尺寸: | 23.4x18.8x2.8 | |||
| 頁數: | 654 | |||
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| 售價: | 1974元 | |||
| 庫存: | 已售完 | |||
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Data Mining (4版)
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126
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詳細資訊
【原文書】 書名：Data Mining: Practical Machine Learning Tools and Techniques 4/e 作者：Witten 出版社：Morgan Kaufmann 出版日期：2016/11/17 ISBN：9780128042915
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【簡介】 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. 【目錄】 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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