| Title : | Data mining for business intelligence : concepts, techniques, and applications in Microsoft Office Excel with XLMiner |
| Material Type: | printed text |
| Authors: | Shmueli Galit, Author ; Nitin R. Patel, Author ; Peter C. Bruce, Author |
| Publisher: | New York : John Wiley & Sons, Inc |
| Publication Date: | 2007 |
| Pagination: | xviii, 279 p. |
| Layout: | ill. |
| Size: | 26 cm. |
| ISBN (or other code): | 978-0-470-08485-4 |
| General note: | Includes bibliographical references (p. 271-272)
Includes index (p. 273-279)
|
| Languages : | English (eng) Original Language : English (eng) |
| Descriptors: | Business - Data processing
Data mining
Microsoft Excel (Computer file)
|
| Class number: | 005.54 |
| Abstract: | Data mining—the art of extracting useful information from large amounts of data—is of growing importance in today's world. Your e-mail spam filter relies at least in part on rules that a data mining algorithm has learned from examining millions of e-mail messages that have been classified as spam or not-spam. Real-time data mining methods enable Web-based merchants to tell you that "customers who purchased x are also likely to purchase y." Data mining helps banks determine which applicants are likely to default on loans, helps tax authorities identify which tax returns are most likely to be fraudulent, and helps catalog merchants target those customers most likely to purchase. And data mining is not just about numbers—text mining techniques help search engines like Google and Yahoo find what you are looking for by ordering documents according to their relevance to your query. In the process they have effectively monetized search by ordering sponsored ads that are relevant to your query. The amount of data flowing from, to, and through enterprises of all sorts is enormous, and growing rapidly—more rapidly than the capabilities of organizations to use it. Successful enterprises are those that make effective use of the abundance of data to which they have access: to make better predictions, better decisions, and better strategies. The margin over a competitor may be small (they, after all, have access to the same methods for making effective use of information), hence the need to take advantage of every possible avenue to advantage. At no time has the need been greater for quantitatively skilled managerial expertise. Successful managers now need to know about the possibilities and limitations of data mining. But at what level? A high-level overview can provide a general idea of what data mining can do for the enterprise but fails to provide the intuition that could be attained by actually building models with real data. |
| Contents note: | Overview of the Data Mining Process; Data Exploration and Dimension Reduction; Evaluating Classification and Predictive Performance; Multiple Linear Regression; Classification and Regression Trees; Logistic Regression; Neural Nets; Discriminant Analysis; Association Rules; Cluster analysis; |
| Record link: | https://library.seeu.edu.mk/index.php?lvl=notice_display&id=14315 |
EBSCOhost
World Bank
EconBiz
De Gruyter
OpenEdition
European Respiratory Journal
Data mining for business intelligence / Shmueli Galit
