Details

Autor(en) / Beteiligte

• Dean, Jared

Titel

Big data, data mining, and machine learning : value creation for business leaders and practitioners

Auflage

1st ed

Ort / Verlag

Hoboken : Wiley,

Erscheinungsjahr

2014

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• Description based upon print version of record.
• Big Data, Data Mining, and Machine Learning; Contents; Forward; Preface; Acknowledgments; Introduction; Big Data Timeline; Why This Topic Is Relevant Now; Is Big Data a Fad?; Where Using Big Data Makes a Big Difference; Technical Issue; Work Flow Productivity; The Complexities When Data Gets Large; Part One The Computing Environment; Chapter 1 Hardware; Storage (Disk); Central Processing Unit; Graphical Processing Unit; Memory; Network; Chapter 2 Distributed Systems; Database Computing; File System Computing; Considerations; Chapter 3 Analytical Tools; Weka; Java and JVM Languages; R; Python
• SASPart Two Turning Data into Business Value; Chapter 4 Predictive Modeling; A Methodology for Building Models; sEMMA; sEMMA for the Big Data Era; Binary Classification; Multilevel Classification; Interval Prediction; Assessment of Predictive Models; Classification; Receiver Operating Characteristic; Lift; Gain; Akaike's Information Criterion; Bayesian Information Criterion; Kolmogorov‐Smirnov; Chapter 5 Common Predictive Modeling Techniques; RFM; Regression; Basic Example of Ordinary Least Squares; Assumptions of Regression Models; Additional Regression Techniques
• Applications in the Big Data EraGeneralized Linear Models; Example of a Probit GLM; Applications in the Big Data Era; Neural Networks; Basic Example of Neural Networks; Decision and Regression Trees; Support Vector Machines; Bayesian Methods Network Classification; Naive Bayes Network; Parameter Learning; Learning a Bayesian Network; Inference in Bayesian Networks; Scoring for Supervised Learning; Ensemble Methods; Chapter 6 Segmentation; Cluster Analysis; Distance Measures (Metrics); Evaluating Clustering; Number of Clusters; K-means Algorithm; Hierarchical Clustering; Profiling Clusters
• Chapter 7 Incremental Response ModelingBuilding the Response Model; Measuring the Incremental Response; Chapter 8 Time Series Data Mining; Reducing Dimensionality; Detecting Patterns; Fraud Detection; New Product Forecasting; Time Series Data Mining in Action: Nike+ FuelBand; Seasonal Analysis; Trend Analysis; Similarity Analysis; Chapter 9 Recommendation Systems; What Are Recommendation Systems?; Where Are They Used?; How Do They Work?; Baseline Model; Low‐Rank Matrix Factorization; Stochastic Gradient Descent; Alternating Least Squares; Restricted Boltzmann Machines; Contrastive Divergence
• Assessing Recommendation QualityRecommendations in Action: SAS Library; Chapter 10 Text Analytics; Information Retrieval; Content Categorization; Text Mining; Text Analytics in Action: Let's Play Jeopardy!; Information Retrieval Steps; Discovering Topics in Jeopardy! Clues; Topics from Clues Having Incorrect or Missing Answers; Discovering New Topics from Clues; Contestant Analysis: Fantasy Jeopardy!; Part Three Success Stories of Putting It All Together; Chapter 11 Case Study of a Large U.S.-Based Financial Services Company; Traditional Marketing Campaign Process
• High-Performance Marketing Solution
• With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computin
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