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150126s2015 nyu foab 001 0 eng d |
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|b eng
|e rda
|e pn
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|d MiAaPQ
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|a HF54.5
|b .M243 2015
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|a 658.4038
|2 23
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|a Maheshwari, Anil,
|d 1949-,
|e author.
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|a Business intelligence and data mining /
|c Anil K. Maheshwari.
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|a First edition.
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|a New York, New York (222 East 46th Street, New York, NY 10017) :
|b Business Expert Press,
|c 2015.
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| 300 |
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|a 1 online resource (xiv, 162 pages)
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| 336 |
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|a text
|2 rdacontent
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|a computer
|2 rdamedia
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| 338 |
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|a online resource
|2 rdacarrier
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1 |
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|a Big data and business analytics collection,
|x 2333-6757
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|a Includes bibliographical references (pages 157-158) and index.
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|a 1. Wholeness of business intelligence and data mining -- 2. Business intelligence concepts and applications -- 3. Data warehousing -- 4. Data mining -- 5. Decision trees -- 6. Regression -- 7. Artificial neural networks -- 8. Cluster analysis -- 9. Association rule mining -- 10. Text mining -- 11. Web mining -- 12. Big data -- 13. Data modeling primer -- Additional resources -- Index.
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|a Access restricted to authorized users and institutions.
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|a Business is the act of doing something productive to serve someone's needs, and thus earn a living, and make the world a better place. Business activities are recorded on paper or using electronic media, and then these records become data. There is more data from customers' responses and on the industry as a whole. All this data can be analyzed and mined using special tools and techniques to generate patterns and intelligence, which reflect how the business is functioning. These ideas can then be fed back into the business so that it can evolve to become more effective and efficient in serving customer needs. And the cycle continues on. Business intelligence includes tools and techniques for data gathering, analysis, and visualization for helping with executive decision making in any industry. Data mining includes statistical and machine-learning techniques to build decision-making models from raw data. Data mining techniques covered in this book include decision trees, regression, artificial neural networks, cluster analysis, and many more. Text mining, web mining, and big data are also covered in an easy way. A primer on data modeling is included for those uninitiated in this topic.
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|a Title from PDF title page (viewed on January 26, 2015).
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|a Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.
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| 650 |
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|a Business information services.
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| 650 |
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|a Data mining.
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| 650 |
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|a Business intelligence.
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|a Data Analytics
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|a Data Mining
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| 653 |
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|a Business Intelligence
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| 653 |
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|a Decision Trees
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| 653 |
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|a Regression
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| 653 |
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|a Neural Networks
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| 653 |
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|a Cluster analysis
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| 653 |
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|a Association rules
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|a Electronic books.
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|i Print version:
|z 9781631571206
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| 797 |
2 |
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|a ProQuest (Firm)
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| 830 |
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|a Big data and business analytics collection.
|x 2333-6757
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| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=1911815
|z Click to View
|
