Principles of Data Mining pdf

英国朴茨茅斯大学信息技术荣誉教授 国际信息处理联盟名誉秘书 英国计算机学会人工智能专业组主席 曾任朴茨茅斯大学数字 IT 教授和信息科学系主任，英国，Hewlett-Labs Packard 知识工程项目主任，布里斯托尔，泰晤士理工学院（现格林威治大学）计算和信息技术学院主席，东米德兰兹地区主任，DES 微电子教育项目，计算机科学讲师，开放大学，

Principles of Data Mining pdf 马克斯·布拉默

This book explains and explores the main techniques of data mining, the automatic extraction of useful implicit and potentially useful information from data, which are increasingly used in commercial, scientific, and other applications. It focuses on classification and mining of link and agglomeration bases. Each topic is clearly explained, with an emphasis on algorithms rather than mathematical formalism, and is illustrated through detailed practical examples. The book is written for readers who do not have a strong background in mathematics or statistics and any formulas used are explained in detail. It can be used as a textbook to support undergraduate or graduate courses in a wide range of subjects including computer science, business studies, marketing, artificial intelligence, bioinformatics, and forensic science. As a self-study aid, this book is intended to help readers in general develop the necessary understanding of what is inside the "black box" so that they can discriminately use commercial data mining packages, as well as to enable advanced readers or academic researchers to understand or contribute to technical advances future in this field. Each chapter contains practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying data flows, both static data, where the base model is fixed, and time-dependent data, where the base model changes from time to time - a phenomenon known as concept skew.