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020			_a9788126577392
082			_a005.54 _bSHM
100			_aShmueli, Galit _9839
245			_aData mining for business analytics: concepts, techniques and applications in R
260			_bWiley India Pvt. Ltd. _aNew Delhi _c2019
300			_axxix, 544 p.
365			_aINR _b899.00
504			_aContents Foreword by Gareth James Foreword by Ravi Bapna Preface to the R Edition Acknowledgments Part I Preliminaries Chapter 1 Introduction 1.1 What Is Business Analytics? 1.2 What Is Data Mining? 1.3 Data Mining and Related Terms 1.4 Big Data 1.5 Data Science 1.6 Why Are There So Many Different Methods? 1.7 Terminology and Notation 1.8 Road Maps to This Book Chapter 2 Overview of the Data Mining Process 2.1 Introduction 2.2 Core Ideas in Data Mining 2.3 The Steps in Data Mining 2.4 Preliminary Steps 2.5 Predictive Power and Overfitting 2.6 Building a Predictive Model 2.7 Using R for Data Mining on a Local Machine 2.8 Automating Data Mining Solutions Part II Data Exploration and Dimension Reduction Chapter 3 Data Visualization 3.1 Uses of Data Visualization 3.2 Data Examples 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 3.4 Multidimensional Visualization 3.5 Specialized Visualizations 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal Chapter 4 Dimension Reduction 4.1 Introduction 4.2 Curse of Dimensionality 4.3 Practical Considerations 4.4 Data Summaries 4.5 Correlation Analysis 4.6 Reducing the Number of Categories in Categorical Variables 4.7 Converting a Categorical Variable to a Numerical Variable 4.8 Principal Components Analysis 4.9 Dimension Reduction Using Regression Models 4.10 Dimension Reduction Using Classification and Regression Trees Part III Performance Evaluation Chapter 5 Evaluating Predictive Performance 5.1 Introduction 5.2 Evaluating Predictive Performance 5.3 Judging Classifier Performance 5.4 Judging Ranking Performance 5.5 Oversampling Part IV Prediction and Classification Methods Chapter 6 Multiple Linear Regression 6.1 Introduction 6.2 Explanatory vs. Predictive Modeling 6.3 Estimating the Regression Equation and Prediction 6.4 Variable Selection in Linear Regression Chapter 7 k-Nearest Neighbors (kNN) 7.1 The k-NN Classifier (Categorical Outcome) 7.2 k-NN for a Numerical Outcome 7.3 Advantages and Shortcomings of k-NN Algorithms Chapter 8 The Naive Bayes Classifier 8.1 Introduction 8.2 Applying the Full (Exact) Bayesian Classifier 8.3 Advantages and Shortcomings of the Naive Bayes Classifier Chapter 9 Classification and Regression Trees 9.1 Introduction 9.2 Classification Trees 9.3 Evaluating the Performance of a Classification Tree 9.4 Avoiding Overfitting 9.5 Classification Rules from Trees 9.6 Classification Trees for More Than Two Classes 9.7 Regression Trees 9.8 Improving Prediction: Random Forests and Boosted Trees 9.9 Advantages and Weaknesses of a Tree Chapter 10 Logistic Regression 10.1 Introduction 10.2 The Logistic Regression Model 10.3 Example: Acceptance of Personal Loan 10.4 Evaluating Classification Performance 10.5 Example of Complete Analysis: Predicting Delayed Flights 10.6 Appendix: Logistic Regression for Profiling Chapter 11 Neural Nets 11.1 Introduction 11.2 Concept and Structure of a Neural Network 11.3 Fitting a Network to Data 11.4 Required User Input 11.5 Exploring the Relationship Between Predictors and Outcome 11.6 Advantages and Weaknesses of Neural Networks Chapter 12 Discriminant Analysis 12.1 Introduction 12.2 Distance of a Record from a Class 12.3 Fisher's Linear Classification Functions 12.4 Classification Performance of Discriminant Analysis 12.5 Prior Probabilities 12.6 Unequal Misclassification Costs 12.7 Classifying More Than Two Classes 12.8 Advantages and Weaknesses Chapter 13 Combining Methods: Ensembles and Uplift Modeling 13.1 Ensembles 13.2 Uplift (Persuasion) Modeling 13.3 Summary Part V Mining Relationships Among Records Chapter 14 Association Rules and Collaborative Filtering 14.1 Association Rules 14.2 Collaborative Filtering 14.3 Summary Chapter 15 Cluster Analysis 15.1 Introduction 15.2 Measuring Distance Between Two Records 15.3 Measuring Distance Between Two Clusters 15.4 Hierarchical (Agglomerative) Clustering 15.5 Non-Hierarchical Clustering: The k-Means Algorithm Part VI Forecasting Time Series Chapter 16 Handling Time Series 16.1 Introduction 16.2 Descriptive vs. Predictive Modeling 16.3 Popular Forecasting Methods in Business 16.4 Time Series Components 16.5 Data-Partitioning and Performance Evaluation Chapter 17 Regression-Based Forecasting 17.1 A Model with Trend 17.2 A Model with Seasonality 17.3 A Model with Trend and Seasonality 17.4 Autocorrelation and ARIMA Models Chapter 18 Smoothing Methods 18.1 Introduction 18.2 Moving Average 18.3 Simple Exponential Smoothing 18.4 Advanced Exponential Smoothing Part VII Data Analytics Chapter 19 Social Network Analytics 19.1 Introduction 19.2 Directed vs. Undirected Networks 19.3 Visualizing and Analyzing Networks 19.4 Social Data Metrics and Taxonomy 19.5 Using Network Metrics in Prediction and Classification 19.6 Collecting Social Network Data with R 19.7 Advantages and Disadvantages Chapter 20 Text Mining 20.1 Introduction 20.2 The Tabular Representation of Text: Term-Document Matrix and "Bag-of-Words" 20.3 Bag-of-Words vs. Meaning Extraction at Document Level 20.4 Preprocessing the Text 20.5 Implementing Data Mining Methods 20.6 Example: Online Discussions on Autos and Electronics 20.7 Summary Part VIII Cases Chapter 21 Cases 21.1 Charles Book Club 21.2 German Credit 21.3 Tayko Software Cataloger 21.4 Political Persuasion 21.5 Taxi Cancellations 21.6 Segmenting Consumers of Bath Soap 21.7 Direct-Mail Fundraising 21.8 Catalog Cross-Selling 21.9 Predicting Bankruptcy 21.10 Time Series Case: Forecasting Public Transportation Demand References Data Files Used in the Book Index
520			_aThis book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.Incorporating an innovative focus on data visualization and time series forecasting, Data Mining for Business Analytics supplies insightful, detailed guidance on fundamental data mining techniques.
650			_aBusiness -- Data processing _9840
650			_aData mining _9365
700			_aBruce, Peter C. _9841
700			_aYahav, Inbal _9842
700			_aPatel, Nitin R. _9843
942			_2ddc _cBK