《实用数据挖掘》

本书对面向应用的数据挖掘方法进行了清晰的阐述，包括经典的多元统计方法、贝叶斯多元统计方法、基于机器学习的数据挖掘方法和基于计算的数据挖掘方法等。介绍了数据挖掘领域中许多最新的研究成果，如关联规则、序列规则、图示马尔可夫模型、基于存储的推理、信用风险和Web挖掘等。并详细介绍了选自实际工业项目的6个应用实例，强调了数据挖掘方法的实用性。 本书主要面向计算机科学、信息管理、应用统计学和经济学等专业的高年级本科生和研究生。对实际从事海量数据分析和处理的技术人员也有很好的指导作用和参考价值。 -Application-oriented book on data mining methods were clearly described, including the classic multivariate statistical methods, Bayesian multivariate statistical methods, data mining based on machine learning methods and calculations based on data mining methods. The field of data mining are introduced many of the latest research results, such as association rules, sequence rules, icons, Markov model, based on the storage of reasoning, credit risk, and Web mining. And gave details of the actual industrial projects selected from the six examples, emphasizing the usefulness of data mining methods. Book is mainly for computer science, information management, applied statistics and economics, and other professional undergraduate and graduate students. Actually engaged in massive data for analysis and processing of the technical staff are also very good guidance and reference value.