Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems-Reference-Cited by-同舟云学术

Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems

Published:2015 Issue: Volume: Page:120-142
ISSN:2327-3429
Container-title:Advances in Business Strategy and Competitive Advantage
language:
Short-container-title:

Author:

Domingues Marcos Aurélio¹,Jorge Alípio Mário²,Soares Carlos²,Rezende Solange Oliveira¹

Affiliation:

1. University of São Paulo, Brazil

2. University of Porto, Portugal

Abstract

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information system that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.

Publisher

IGI Global

Reference43 articles.

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of Twentieth International Conference on Very Large Data Bases (pp. 487-499). Academic Press.

2. Anand, S. S., & Mobasher, B. (2003). Intelligent techniques for web personalization. In Intelligent Techniques for Web Personalization (LNCS), (vol. 3169, pp. 1-36). Berlin: Springer.

3. A Perspective on Data Mining Integration with Business Intelligence;A.Azevedo;Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains,2011

4. Berendt, B., Mobasher, B., Nakagawa, M., & Spiliopoulou, M. (2002). The impact of site structure and user environment on session reconstruction in web usage analysis. In Proceedings of WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles (pp. 159-179). Springer.

5. Berners-Lee, T., & Connolly, D. (1995). Hypertext markup language - 2.0. Technical report rfc 1866. Retrieved July 12, 2013, from http://www.ietf.org/rfc/rfc1866.txt

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Electronic Commerce and Decision Support Systems;Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business;2021

2. Electronic Commerce and Decision Support Systems;Improving E-Commerce Web Applications Through Business Intelligence Techniques;2018

3. Mining Drug Properties for Decision Support in Dental Clinics;Advances in Knowledge Discovery and Data Mining;2017