feijie7788的博客

本书由实战经验丰富的两位数据分析师执笔，首先介绍了商业领域里通用的数据分析框架，然后根据该框架，结合8个真实的案例，详细解说了通过数据分析解决各种商业问题的流程，让读者在解决问题的过程中学习各种数据分析方法，包括柱状图、交叉列表统计、A/B测试、多元回归分析、逻辑回归分析、主成分分析、聚类、决策树分析、机器学习等。特别是书中使用的数据都是未经清洗的原始数据，能够让读者了解真实的数据分析流程，避免纸上谈兵。

HANDBOOK OF STATISTICAL ANALYSIS AND DATA MINING APPLICATIONS “Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra DVD material make this book an invaluable resource for both new and experienced data miners.” Karl Rexer, Ph.D. (President and Founder of Rexer Analytics, Boston, Massachusetts, www.RexerA) “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” H. G. Wells (1866 – 1946) “Today we aren’t quite to the place that H. G. Wells predicted years ago, but society is getting closer out of necessity. Global businesses and organizations are being forced to use statistical analysis and data mining applications in a format that combines art and science–intuition and expertise in collecting and understanding data in order to make accurate models that realistically predict thefuture that lead to informed strategic decisions thus allowing correct actions ensuring success, before it is too late . . . today, numeracy is as essential as literacy. As John Elder likes to say: ‘Go data mining!’ It really does save enormous time and money. For those with the patience and faith to get through the early stages of business understanding and data transformation, the cascade of results can be extremely rewarding.” Gary Miner, March, 2009 HANDBOOK OF STATISTICAL ANALYSIS AND DATA MINING APPLICATIONS ROBERT NISBET Pacific Capital Bankcorp N.A.

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor.