skip to main content
article
Free Access

Emerging trends in business analytics

Published:01 August 2002Publication History
Skip Abstract Section

Abstract

The goal is business effectiveness through 'verticalization,' usability, and integration with operational systems.

References

  1. Becker, B., Kohavi, R., and Sommerfield, D. Visualizing the simple Bayesian classifier. In Information Visualization in Data Mining and Knowledge Discovery, chapt. 18, U. Fayyad, G. Grinstein, and A. Wierse, Eds. Morgan Kaufmann Publishers, San Francisco, 2001, 237--249. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Berry, M. and Linoff, G. Mastering Data Mining. John Wiley & Sons, Inc., New York, 2000.Google ScholarGoogle Scholar
  3. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining, chapt. 1, U. Fayyad, G. Piatesky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. AAAI Press, Menlo Park, CA, and the MIT Press, Cambridge, MA, 1996, 1--34. . Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Kimball, R. and Merz, R. The Data Webhouse Toolkit: Building the Web-Enabled Data Warehouse. John Wiley & Sons, Inc., New York, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Kohavi, R., Brodley, C., Frasca, B., Mason, L., and Zheng, Z. KDD-Cup 2000 organizers' report: Peeling the onion. SIGKDD Explor. 2, 2 (Dec. 2000), 86--98; see www.ecn.purdue.edu/KDDCUP. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Kohavi, R. and Provost, F. Applications of data mining to electronic commerce. Data Min. Knowl. Disc. 5, 1/2 (Jan.-Apr. 2001); see robotics.stanford.edu/users/ronnyk/ecommerce-dm. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Lee, J., Podlaseck, M., Schonberg, E., and Hoch, R. Visualization and analysis of clickstream data of online stores for understanding Web merchandising. Data Min. Knowl. Discov. 5, 1/2 (Jan.-Apr. 2001). Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Souza, R., Manning, H., and Gardiner, K. How to measure what matters. Forrester Rep. (May 2001).Google ScholarGoogle Scholar
  9. Thearling, K., Becker, B., DeCoste, D., Mawby, B., Pilote, M., and Sommerfield. D. Visualizing data mining models. In Information Visualization in Data Mining and Knowledge Discovery, U. Fayyad, G. Grinstein, and A. Wierse, Eds. Morgan Kaufmann Publishers, San Francisco, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Emerging trends in business analytics

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image Communications of the ACM
          Communications of the ACM  Volume 45, Issue 8
          Evolving data mining into solutions for insights
          August 2002
          96 pages
          ISSN:0001-0782
          EISSN:1557-7317
          DOI:10.1145/545151
          Issue’s Table of Contents

          Copyright © 2002 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 1 August 2002

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • article

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format .

        View HTML Format