Introduction to Data Mining, Second Edition

出版者:Pearson

作者:Pang-Ning Tan

页数:864

出版时间:2018-1-4

价格:USD 126.65

装帧:Hardcover

isbn号码:9780133128901

具体描述

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.

作者简介

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.

读后感

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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。...  

这本书写得逻辑性比较强，全面，而且我觉得涉及的东西也比较底层，让我们了解一些算法的基本型原理是非常重要的。如果，网上的机器学习相关文章看不懂的话，可以从这本书入手。中文版的只看过一点点，感觉完全没逻辑性，完全没感觉。翻译出来完全就变味了，毕竟是语言习惯上的...  

Chapter2 和 Chapter3 一大堆废话，基本都是初中高中教的！！！好像跳过这些章节！！！ Chapter2 和 Chapter3 一大堆废话，基本都是初中高中教的！！！好像跳过这些章节！！！ Chapter2 和 Chapter3 一大堆废话，基本都是初中高中教的！！！好像跳过这些章节！！！  

我的习惯就是在蹲坑的时候读一些艰涩高深的科学读物，这样有助于我在排泄的时候大脑保持高度的兴奋状态，不至于被熏晕或者不至于被引人入胜的小说情节所陶醉最后导致肛瘘…… 但是，这本书另我惊诧了…… 第一他不艰涩，是我读到过的关于统计、关于数据、关于计算的最科普的读...