Learning.Data.Mining.with.R

【文件属性】：
文件名称：Learning.Data.Mining.with.R
文件大小：6.98MB
文件格式：PDF
更新时间：2018-03-30 04:18:43
Data Mining R Title: Learning Data Mining with R Author: Bater Makhabel Length: 380 pages Edition: 1 Language: English Publisher: Packt Publishing Publication Date: 2014-12-22 ISBN-10: 1783982101 ISBN-13: 9781783982103 Develop key skills and techniques with R to create and customize data mining algorithms About This Book Develop a sound strategy for solving predictive modeling problems using the most popular data mining algorithms Gain understanding of the major methods of predictive modeling Packed with practical advice and tips to help you get to grips with data mining Who This Book Is For This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes. In Detail Being able to deal with the array of problems that you may encounter during complex statistical projects can be difficult. If you have only a basic knowledge of R, this book will provide you with the skills and knowledge to successfully create and customize the most popular data mining algorithms to overcome these difficulties. You will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this book feeling confident in your ability to know which data mining algorithm to apply in any situation. Table of Contents Chapter 1: Warming Up Chapter 2: Mining Frequent Patterns, Associations, and Correlations Chapter 3: Classification Chapter 4: Advanced Classification Chapter 5: Cluster Analysis Chapter 6: Advanced Cluster Analysis Chapter 7: Outlier Detection Chapter 8: Mining Stream, Time-series, and Sequence Data Chapter 9: Graph Mining and Network Analysis Chapter 10: Mining Text and Web Data Appendix: Algorithms and Data Structures