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Data mining for business analytics : concepts, techniques, and applications with XLMiner®.

By: Contributor(s): Material type: TextOriginal language: English Publisher: New Jersey : John Wiley & Sons, 2016Edition: 3Description: 514 páginas : : figuras ; tablesContent type:
  • texto
Media type:
  • sin mediación
Carrier type:
  • volumen
ISBN:
  • 9781118729274
Subject(s): DDC classification:
  • 23 005.54 S558
Contents:
CHAPTER 1 Introduction -- CHAPTER 2 Overview of the Data Mining Process. -- CHAPTER 3 Data Visualization. -- CHAPTER 4 Dimension Reduction. -- CHAPTER 5 Evaluating Predictive Performance. -- CHAPTER 6 Multiple Linear Regression. -- CHAPTER 7 k-Nearest Neighbors (kNN). -- CHAPTER 8 The Naive Bayes Classifier. -- CHAPTER 9 Classification and Regression Trees. -- CHAPTER 10 Logistic Regression. -- CHAPTER 11 Neural Nets. -- CHAPTER 12 Discriminant Analysis. -- CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling. -- CHAPTER 14 Association Rules and Collaborative Filtering. -- CHAPTER 15 Cluster Analysis. -- CHAPTER 16 Handling Time Series. -- CHAPTER 17 Regression-Based Forecasting. -- CHAPTER 18 Smoothing Methods. -- CHAPTER 19 Social Network Analytics. -- CHAPTER 20 Text Mining. -- CHAPTER 21 Cases.
Summary: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
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Cover image Item type Current library Home library Collection Shelving location Call number Materials specified Vol info URL Copy number Status Notes Date due Barcode Item holds Item hold queue priority Course reserves
LIBROS - MATERIAL GENERAL BIBLIOTECA CENTRAL General 005.54 S558 (Browse shelf(Opens below)) Ej.:1 Available 086921
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Contents.

CHAPTER 1 Introduction -- CHAPTER 2 Overview of the Data Mining Process. -- CHAPTER 3 Data Visualization. -- CHAPTER 4 Dimension Reduction. -- CHAPTER 5 Evaluating Predictive Performance. -- CHAPTER 6 Multiple Linear Regression. -- CHAPTER 7 k-Nearest Neighbors (kNN). -- CHAPTER 8 The Naive Bayes Classifier. -- CHAPTER 9 Classification and Regression Trees. -- CHAPTER 10 Logistic Regression. -- CHAPTER 11 Neural Nets. -- CHAPTER 12 Discriminant Analysis. -- CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling. -- CHAPTER 14 Association Rules and Collaborative Filtering. -- CHAPTER 15 Cluster Analysis. -- CHAPTER 16 Handling Time Series. -- CHAPTER 17 Regression-Based Forecasting. -- CHAPTER 18 Smoothing Methods. -- CHAPTER 19 Social Network Analytics. -- CHAPTER 20 Text Mining. -- CHAPTER 21 Cases.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.

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