Predictive analytics and data mining : concepts and practice with RapidMiner [printed text] / Vijay Kotu, Author ; Bala Deshpande, Author . - Oxford : Elsevier Butterworth-Heinemann, 2015 . - xix, 423 p. : ill. ; 18mm.
ISBN : 978-0-12-801460-8 : €50.46
Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You'll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool

Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com

Class number:

006.312

Contents note:

Chapter 1: Introduction 1.1. What data mining is 1.2. What data mining is not 1.3. The case for data mining 1.4. Types of data mining 1.5. Data mining algorithms 1.6. Roadmap for upcoming chapters Chapter 2: Data mining process 2.1. Prior knowledge 2.2. Data preparation 2.3. Modeling 2.4. Application 2.5. Knowledge Chapter 3: Data Exploration 3.1. Objectives of data exploration 3.2. Data sets 3.3. Descriptive statistics 3.4. Data visualisation 3.5. Roadmap for data exploration Chapter 4: Classification 4.1. Decision trees 4.2. Rule induction 4.3. k-Nearest neighbors 4.4. Naïve Bayesian 4.5. Artificial neural networks 4.6. Support vector machines 4.7. Ensemble learners Chapter 5: Regression methods 5.1. Linear regression 5.2. Logistic regression Chapter 6: Association analysis 6.1. Concepts of mining association rules 6.2. Apriori algorithm 6.3. FP-Growth algorithm Chapter 7: Clustering 7.1. Types of clustering techniques 7.2. k-Means clustering 7.3. DBSCAN clustering 7.4. Self-organising maps Chapter 8: Model evaluation 8.1. Confusion matrix [or truth tables] 8.2. Receiver operator characteristics [ROC] curves and area under the curve [AUC] 8.3. Lift curves 8.4. Evaluating the predictions: implementation Chapter 9: Text mining 9.1. How text mining works 9.2. Implementing text mining with clustering and classification Chapter 10: Time series forecasting 10.1. Data-driven approaches 10.2. Model-driven forecasting methods Chapter 11: Anomaly detection 11.1. Anomlay detection concepts 11.2. Distance-based outlier detection 11.3. Density-based outlier detection 11.4. Local outlier factor Chapter 12: Feature selection 12.1. Classifying feature selection methods 12.2. Principal component analysis 12.3. Information theory-based filter for numeric data 12.4. Chi-square-based filtering for categorical data 12.5. Wrapper-type feature selection Chapter 13: Getting started with RapidMiner 13.1. User interface and terminology 13.2. Data importing and exporting tools 13.3. Data visualization tools 13.4. Data transformation tools 13.5. Sampling and missing value tools 13.6. Optimization tools