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Discovering knowledge in data: an introduction to data mining
Abstract
The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and VisualizationOffers extensive coverage of the R statistical programming languageContains 280 end-of-chapter exercisesIncludes a companion website with further resources for all readers, and Powerpoint slides, a solutions manual, and suggested projects for instructors who adopt the book
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Cited by
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Applications of artificial intelligence in transport: An overview
Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019) · Sustainability · doi:10.3390/su11010189 · Cited by 769
A comparison of machine learning techniques for customer churn prediction
Vafeiadis, T., Diamantaras, K. I., Sarigiannidis, G., & Chatzisavvas, K. C. (2015) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2015.03.003 · Cited by 471
Visualizing big data with augmented and virtual reality: Challenges and research agenda
Olshannikova, E., Ometov, A., Koucheryavy, Y., & Olsson, T. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0031-2 · Cited by 315
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Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 2005-04-01
- Volume
- 42
- Issue
- 08
- Pages
- 42-4687-42-4687
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W1673075472
- Cited by
- 1,347
- References
- 0
- Type
- book review
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
