By Daniel T. Larose
- The moment variation of a hugely praised, winning reference on info mining, with thorough insurance of huge information purposes, predictive analytics, and statistical analysis.
- Includes new chapters on Multivariate records, getting ready to version the information, and Imputation of lacking information, and an Appendix on facts Summarization and Visualization
- Offers huge assurance of the R statistical programming language
- Contains 280 end-of-chapter exercises
- Includes a spouse web site with additional assets for all readers, and Powerpoint slides, a suggestions guide, and recommended initiatives for teachers who undertake the book
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Extra resources for Discovering knowledge in data : an introduction to data mining
Larose. © 2014 John Wiley & Sons, Inc. Published 2014 by John Wiley & Sons, Inc. 2 DATA CLEANING 17 Chapter 1 introduced us to data mining, and the CRISP-DM standard process for data mining model development. In Phase 1 of the data mining process, business understanding or research understanding, businesses and researchers first enunciate project objectives, then translate these objectives into the formulation of a data mining problem definition, and finally prepare a preliminary strategy for achieving these objectives.
The data analyst should choose a reclassification that supports the objectives of the business problem or research question. 17 ADDING AN INDEX FIELD It is recommended that the data analyst create an index field, which tracks the sort order of the records in the database. Data mining data gets partitioned at least once (and sometimes several times). It is helpful to have an index field so that the original sort order may be recreated. For example, using IBM/SPSS Modeler, you can use the @Index function in the Derive node to create an index field.
R For an ultra-light vehicle, weighing only 1613 pounds (the field minimum), the min-max normalization is ∗ Xmm = X − min(X) 1613 − 1613 = =0 range(X) 3384 Thus, data values that represent the minimum for the variable will have a min-max normalization value of zero. r The midrange equals the average of the maximum and minimum values in a data set. 5. r The heaviest vehicle has a min-max normalization value of ∗ Xmm = X − min(X) 4497 − 1613 = =1 range(X) 3384 That is, data values representing the field maximum will have a min-max normalization of 1.