Abstract
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.-- Provided by Publisher.
Original language | English |
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Place of Publication | Hoboken, New Jersey |
Number of pages | 577 |
ISBN (Electronic) | 9781118879368, 9781118956632 |
State | Published - 2018 |
Externally published | Yes |
ULI publications
- uli
- Algorithmic knowledge discovery
- Business -- Data processing
- Business mathematics -- Computer programs
- Data mining
- Electronic data processing -- Business
- Factual data analysis
- GNU-S (Computer program language)
- KDD (Information retrieval)
- Knowledge discovery in data
- Knowledge discovery in databases
- Mining, Data
- R (Computer program language)