Introduction to Data Mining, Second Edition pdf epub mobi txt 電子書 下載2025
Dr Pang-Ning Tan is a Professor in the Department of Computer Science and Engineering at Michigan State University. He received his M.S. degree in Physics and Ph.D. degree in Computer Science from University of Minnesota. 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. He has published more than 130 technical papers in the area of data mining, including top conferences and journals such as KDD, ICDM, SDM, CIKM, and TKDE.
Dr. Michael Steinbach is a Research Scientist in the department of Computer Science and Engineering at the University of Minnesota, from which he earned a B.S. degree in Mathematics, an M.S. degree in Statistics, and M.S. and Ph.D. degrees in Computer Science. 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. This research has resulted in more than 100 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.
Dr. Anuj Karpatne is a Post Doctoral Associate in the Department of Computer Science and Engineering at the University of Minnesota. He received his M.Tech in Mathematics and Computing from the Indian Institute of Technology Delhi, and a Ph.D. in Computer Science at the University of Minnesota under the guidance of Prof. 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. His research has been published at top-tier journals and conferences such as SDM, ICDM, KDD, NIPS, TKDE, and ACM Computing Surveys.
- 數據挖掘
- 機器學習
- 數據科學
- Data_Science
- 計算機科學
- 英文原版
- 數據分析
- USC567
Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals. Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps readers understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.
具體描述
讀後感
该书特点：以实例为重，给出了常用算法的伪代码，和《模式识别》、《模式分类》等专著比起来，该书略去了各个定理的证明部分，并通过大量枚举具体的分类实例，来简要说明算法的流程和意义。 根据个人的体验，觉得这本书作为第一本数据挖掘的入门读物是再恰当不过的了。...
評分The book is used as a textbook for my data mining class. It covers all fundamental theories and concepts of data mining, and it explained everything in a quite easy-to-understand and detailed manner. It is suggested to have a good comprehension of some math...
評分给出了DataMining的一般性解决思路，全面易懂，很适合给初学者扫盲。加之与原版大概400+RMB比较起来，不禁觉得还是祖国好哇。。。PS:据说巴基斯坦卖得更便宜。。。
評分屎一样狗屁不通的翻译。 原文： As a result, Z is as likely to be chosen for splitting as the interacting but useful attributes, X and Y. 译文：因此，Z 可能被选作划分有相互作用但有效的属性 X 和 Y。 还有其他很多地方就不一一列举了，本来作为入门读物，很多东西就...
評分该书特点：以实例为重，给出了常用算法的伪代码，和《模式识别》、《模式分类》等专著比起来，该书略去了各个定理的证明部分，并通过大量枚举具体的分类实例，来简要说明算法的流程和意义。 根据个人的体验，觉得这本书作为第一本数据挖掘的入门读物是再恰当不过的了。...
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