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das subrata - computational business analytics

Computational Business Analytics




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Dettagli

Genere:Libro
Lingua: Inglese
Pubblicazione: 12/2013
Edizione: 1° edizione





Note Editore

Learn How to Properly Use the Latest Analytics Approaches in Your Organization Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies. The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text: Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks Embeds decision trees within influence diagrams Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns.




Sommario

Analytics Background and Architectures ANALYTICS DEFINED ANALYTICS MODELING ANALYTICS PROCESSES ANALYTICS AND DATA FUSION Mathematical and Statistical Preliminaries STATISTICS AND PROBABILITY THEORY LINEAR ALGEBRA FUNDAMENTALS MATHEMATICAL LOGIC GRAPHS AND TREES MEASURES OF PERFORMANCE ALGORITHMIC COMPLEXITY Statistics for Descriptive Analytics PROBABILITY DISTRIBUTIONS DISCRETE PROBABILITY DISTRIBUTIONSCONTINUOUS PROBABILITY DISTRIBUTIONSGOODNESS-OF-FIT TEST Bayesian Probability and Inference BAYESIAN INFERENCE PRIOR PROBABILITIES Inferential Statistics and Predictive Analytics CHISQUARETEST OF INDEPENDENCE REGRESSION ANALYSESBAYESIAN LINEAR REGRESSION PRINCIPAL COMPONENT AND FACTOR ANALYSES SURVIVAL ANALYSIS AUTOREGRESSION MODELS Artificial Intelligence for Symbolic Analytics ANALYTICS AND UNCERTAINTIES NEO-LOGICIST APPROACH NEO-PROBABILISTNEO-CALCULIST APPROACH NEO-GRANULARIST Probabilistic Graphical Modeling NAIVE BAYESIAN CLASSIFIER (NBC) KDEPENDENCE NAIVE BAYESIAN CLASSIFIER (KNBC) BAYESIAN BELIEF NETWORKS Decision Support and Prescriptive Analytics EXPECTED UTILITY THEORY AND DECISION TREES INFLUENCE DIAGRAMS FOR DECISION SUPPORT SYMBOLIC ARGUMENTATION FOR DECISION SUPPORT Time Series Modeling and Forecasting PROBLEM MODELING KALMAN FILTER (KF) MARKOV MODELSDYNAMIC BAYESIAN NETWORKS (DBNS) Monte Carlo Simulation MONTE CARLO APPROXIMATION GIBBS SAMPLING METROPOLIS-HASTINGS ALGORITHM PARTICLE FILTER (PF) Cluster Analysis and Segmentation HIERARCHICAL CLUSTERING K-MEANS CLUSTERING K-NEAREST NEIGHBORS SUPPORT VECTOR MACHINESNEURAL NETWORKS Machine Learning for Analytics ModelsDECISION TREESLEARNING NAIVE BAYESIAN CLASSIFIERS LEARNING OF KNBC BAYESIAN BELIEF NETWORKSINDUCTIVE LOGIC PROGRAMMING Unstructured Data and Text Analytics INFORMATION STRUCTURING AND EXTRACTION BRIEF INTRODUCTION TO NLPTEXT CLASSIFICATION AND TOPIC EXTRACTION Semantic Web RESOURCE DESCRIPTION FRAMEWORK (RDF) DESCRIPTION LOGICS Analytics Tools INTELLIGENT DECISION AIDING SYSTEM (IDAS) ENVIRONMENT FOR FIFTH GENERATION APPLICATIONS (E5)ANALYSIS OF TEXT (ATEXT) R AND MATLAB SAS AND WEKA Analytics Case StudiesRISK ASSESSMENT MODEL I3 RISK ASSESSMENT IN INDIVIDUAL LENDING USING IDAS RISK ASSESSMENT IN COMMERCIAL LENDING USING E5 AND IDAS FRAUD DETECTIONSENTIMENT ANALYSIS USING ATEXT Appendix A: Usage of SymbolsAppendix B: Examples and Sample DataAppendix C: MATLAB and R Code Examples Index Further Reading appears at the end of each chapter.




Autore

Subrata Das is the founder and president of Machine Analytics and also serves as a consulting scientist to other companies. He has many years of experience in industrial, government, and academic research and development. He earned his Ph.D. in computer science and master's in mathematics.










Altre Informazioni

ISBN:

9781439890707

Condizione: Nuovo
Collana: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Dimensioni: 9.25 x 6.25 in Ø 1.85 lb
Formato: Copertina rigida
Illustration Notes:290 b/w images and 67 tables
Pagine Arabe: 516


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