Big data, data mining and machine learning : value creation for business leaders and practitioners / Jared Dean
- Author
- Dean, Jared, 1978-
- Published
- Hoboken, New Jersey : John Wiley and Sons, Inc., [2014]
- Physical Description
- 1 online resource
Access Online
- John Wiley: dx.doi.org
- Series
- Contents
- Machine generated contents note: Big Data Timeline -- Why This Topic Is Relevant Now -- Is Big Data a Fad? -- Where Using Big Data Makes a Big Difference -- pt. One The Computing Environment -- ch. 1 Hardware -- Storage (Disk) -- Central Processing Unit -- Memory -- Network -- ch. 2 Distributed Systems -- Database Computing -- File System Computing -- Considerations -- ch. 3 Analytical Tools -- Weka -- Java and JVM Languages -- R -- Python -- SAS -- pt. Two Turning Data into Business value -- ch. 4 Predictive Modeling -- A Methodology for Building Models -- sEMMA -- Binary Classification -- Multilevel Classification -- Interval Prediction -- Assessment of Predictive Models -- ch. 5 Common Predictive Modeling Techniques -- RFM -- Regression -- Generalized Linear Models -- Neural Networks -- Decision and Regression Trees -- Support Vector Machines -- Bayesian Methods Network Classification -- Ensemble Methods -- ch. 6 Segmentation -- Cluster Analysis -- Distance Measures (Metrics) -- Evaluating Clustering -- Number of Clusters -- K-means Algorithm -- Hierarchical Clustering -- Profiling Clusters -- ch. 7 incremental Response Modeling -- Building the Response Model -- Measuring the Incremental Response -- ch. 8 Time Series Data Mining -- Reducing Dimensionality -- Detecting Patterns -- Time Series Data Mining in Action: Nike+ FuelBand -- ch. 9 Recommendation Systems -- What Are Recommendation Systems? -- Where Are They Used? -- How Do They Work? -- Assessing Recommendation Quality -- Recommendations in Action: SAS Library -- ch. 10 Text Analytics -- Information Retrieval -- Content Categorization -- Text Mining -- Text Analytics in Action: Let's Play Jeopardy! -- pt. Three Success stories of Putting It All Together -- ch. 11 Case Study of a Large U.S.-Based Financial Services Company -- Traditional Marketing Campaign Process -- High-Performance Marketing Solution -- Value Proposition for Change -- ch. 12 Case Study of a Major Health Care Provider -- Cahps -- Hedis -- Hos -- Ire -- ch. 13 Case Study of a Technology Manufacturer -- Finding Defective Devices -- How They Reduced Cost -- ch. 14 Case Study of Online Brand Management -- ch. 15 Case Study of Mobile Application Recommendations -- ch. 16 Case Study of a High-Tech Product Manufacturer -- Handling the Missing Data -- Application beyond Manufacturing -- ch. 17 Looking to the Future -- Reproducible Research -- Privacy with Public Data Sets -- The Internet of Things -- Software Development in the Future -- Future Development of Algorithms -- In Conclusion.
- Summary
- "An expert guide to high performance computing architectures and how they relate to analytics and data miningWith the exponential growth of data comes an ever-increasing need to process and analyze so-called Big Data. High Performance Data Mining and Big Data Analytics provides a comprehensive view of the recent trend toward high performance computing architectures and its natural connection to analytics and data mining. You'll find coverage of topics including: big data, high performance computing for analytics, massively parallel processing (MPP) databases, in-memory analytics, implementation of machine learning algorithms for big data platforms, text analytics, analytics environments, the analytics lifecycle, general applications, as well as a variety of cases. Offers coverage of business analytics, predictive modeling, and fact-based management Includes case studies featuring multinational companies Explores recent trends in high performance computing architectures relating to data mining Filled with case studies, High Performance Data Mining and Big Data Analytics provides a thorough grounding for optimally putting data mining and big data analytics to work for your organization"--
- Subject(s)
- ISBN
- 9781118920695 electronic bk.
1118920694 electronic bk.
9781118920701 electronic bk.
1118920708 electronic bk.
9781118618042 (hardback)
9781118691786
1118691784 - Note
- Includes index.
AVAILABLE ONLINE TO AUTHORIZED PSU USERS. - Bibliography Note
- Includes bibliographical references.
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