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Big data, data mining, and machine learning : value creation for business leaders and practitioners
| Big data, data mining, and machine learning : value creation for business leaders and practitioners |
| Autore | Dean Jared |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Hoboken : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (289 pages) |
| Disciplina |
658
658.05631 658/.05631 |
| Collana |
Wiley and SAS business series
THEi Wiley ebooks |
| Soggetto topico |
Big data
COMPUTERS / Database Management / Data Mining Data mining Database management Information technology -- Management Management -- Data processing Management |
| ISBN | 1-118-69178-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910132334903321 |
Dean Jared
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| Hoboken : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Discovering data mining : from concept to implementation / Peter Cabena...[et al.]
| Discovering data mining : from concept to implementation / Peter Cabena...[et al.] |
| Pubbl/distr/stampa | Upper Saddle River : Prentice Hall, 1997 |
| Descrizione fisica | 195 p. ; 24 cm |
| Disciplina | 658.05631 |
| Soggetto non controllato |
Informatica applicata alla statistica
Intelligenza artificiale |
| ISBN | 0-13-743980-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-990003829120403321 |
| Upper Saddle River : Prentice Hall, 1997 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Discovering data mining : from concept to implementation / Cabena Peter… [et al.].
| Discovering data mining : from concept to implementation / Cabena Peter… [et al.]. |
| Pubbl/distr/stampa | Upper Saddle River : Prentice Hall, 1997 |
| Descrizione fisica | XIX, 195 p. ; 24 cm |
| Disciplina | 658.05631 |
| Soggetto non controllato | marketing Elaborazione dei dati |
| ISBN | 0-13-743980-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNISA-990000016120203316 |
| Upper Saddle River : Prentice Hall, 1997 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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