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- Title: Data Mining with R: Learning with Case Studies
- Author(s) Luis Torgo
- Publisher: University of Porto
- Hardcover: N/A
- eBook: PDF
- Language: English
- ISBN-10: N/A
- ISBN-13: N/A
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Introduce the reader to the use of R as a tool for performing data mining and statistical computing and graphics. The large set of available packages make this tool an excellent alternative to the existing (and expensive!) data mining tools.
About the Authors- N/A
- Data Analysis and Data Mining
- R Programming
- Statistics, Mathematical Statistics, and SAS Programming
- Probability, Stochastic Process, Queueing Theory, etc.
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- Data Mining with R: Learning with Case Studies (Luis Torgo)
- The Mirror Site (1) - PDF
- The Mirror Site (2) - PDF
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Data Mining Algorithms in R (WikiBooks)
This book presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R programming language.
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Text Mining with R: A Tidy Approach (Julia Silge, et al)
You'll explore text-mining techniques with tidytext, a package that authors developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.
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Doing Data Science in R: An Introduction for Social Scientists
This approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. It builds knowledge and skills gradually.
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Data Mining and Analysis: Fundamental Concepts and Algorithms
This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.
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Mining of Massive Datasets (Jure Leskovec, et al)
It focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically.
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Data Mining for the Masses (Matthew North)
This book uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions.
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Spectral Feature Selection for Data Mining (Zheng A. Zhao, et al.)
This book introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications.
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A Programmer's Guide to Data Mining (Ron Zacharski)
This book is a tool for learning basic data mining techniques. If you are a programmer interested in learning a bit about data mining you might be interested in a beginner's hands-on guide as a first step. That's what this book provides.
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An Introduction to Data Mining (Dr. Saed Sayad)
This book presents fundamental concepts and algorithms for those learning data mining for the first time, provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures.
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Data Mining Desktop Survival Guide (Graham William)
This book thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. Assuming no prior knowledge of R or data mining/statistical techniques.
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Mining Social Media: Finding Stories in Internet Data
This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the Pandas library.
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Social Media Mining: An Introduction (Reza Zafarani, et al)
This textbook introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.
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Mining the Web: Discovering Knowledge from Hypertext Data
This is is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issues-including Web crawling and indexing.
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O'Reilly® Mining the Social Web, 2nd Edition (Matthew A. Russell)
This book shows you how to answer these questions like how can you tap into social data and discover who's connecting with whom, which insights are lurking just beneath the surface, and what people are talking about?
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