TY  - BOOK
AU  - Shmueli,Galit
AU  - Bruce,Peter C.
AU  - Patel,Nitin R.
TI  - Data mining for business analytics: concepts, techniques, and applications with XLMiner®
SN  - 9781118729274
U1  - 005.54 23
PY  - 2016///
CY  - New Jersey
PB  - John Wiley & Sons
KW  - Hojas de cálculo electrónicas
KW  - Electronic spreadsheets
KW  - Hojas de cálculo
KW  - programas informáticos
KW  - Spreadsheets
KW  - Computer programs
KW  - software informático
KW  - Computer software
N1  - 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
N2  - 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
ER  -