Abstract
This article describes a novel way of combining data mining techniques on Internet data in order to discover actionable marketing intelligence in electronic commerce scenarios. The data that is considered not only covers various types of server and web meta information, but also marketing data and knowledge. Furthermore, heterogeneity resolution thereof and Internet- and electronic commerce-specific pre-processing activities are embedded. A generic web log data hypercube is formally defined and schematic designs for analytical and predictive activities are given. From these materialised views, various online analytical web usage data mining techniques are shown, which include marketing expertise as domain knowledge and are specifically designed for electronic commerce purposes.
- {AB98} S. S. Anand, A. G. Büchner. Decision Support through Data Mining, FT Pitman Publishers, 1998.Google Scholar
- {ABH95} S. S. Anand, D. A. Bell, J. G. Hughes. The Role of Domain Knowledge in Data Mining, in Proc. 4th Int'l. ACM Conf. on Information and Knowledge Management, pp. 37-43, 1995. Google ScholarDigital Library
- {APHB98} S. S. Anand, A. R. Patrick. J. G. Hughes, D. A. Bell. A Data Mining Methodology for Cross Sales, Knowledge-based Systems Journal, 10: 449-461, 1998.Google ScholarDigital Library
- {AS95} R. Agrawal, R. Srikant. Mining Sequential Patterns, in Proc. 11th Int'l. Conf. on Data Engineering, pp. 3-14, 1995. Google ScholarDigital Library
- {AST+97} S. S. Anand, B. W. Scotney, M. G. Tan, S. I. McClean, D. A. Bell, J. G. Hughes, I. C. Magill. Designing a Kernel for Data Mining, in IEEE Expert, 12(2): 65-74. 1997. Google ScholarDigital Library
- {BBH98} A. G. Büchner, D. A. Bell, J. G. Hughes. A Contextualised Object Data Model based on Semantic Values, in Proc. 11th Int'l. Conf. on Parallel and Distributed Computing Systems, pp. 171-176, 1998.Google Scholar
- {BMAH98} A. G. Büchner, M. D. Mulvenna, S. S. Anand. J. G. Hughes. An Internet-enabled Knowledge Discovery Process, submitted for publication. 1998.Google Scholar
- {BS97} A. Berson, S. J. Smith. Data Warehousing. Data Mining and OLAP, McGraw Hill, 1997. Google ScholarDigital Library
- {CD97} S. Chaudhuri, U. Dayal. An Overview of Data Warehousing and OLAP Technology, Technical Report MSR-TR-97-14, Microsoft Research, 1997.Google ScholarDigital Library
- {CMS97} R. Cooley, B. Mobasher, J. Srivastava. Web Mining: Information and Pattern Discovery on the World Wide Web, in Proc. 9th IEEE Int'l Conf. on Tools with Artificial Intelligence, 1997. Google ScholarDigital Library
- {CMS99} R. Cooley, B. Mobasher, J. Srivastava. Data Preparation for Mining World Wide Web Browsing Patterns, in Knowledge and Information Systems, 1(1), forthcoming, 1999.Google Scholar
- {CPY96} M. S. Chen, J. S. Park, P. S. Yu. Data Mining for Traversal Patterns in a Web Environment, in Proc. 16th Int'l. Conf. on Distributed Computing Systems, pp. 385-392, 1996. Google ScholarDigital Library
- {Etz96} O. Etzioni. The World-Wide Web: Quagmire or Gold Mine?. in Comm. of the ACM, 39(11): 65-68, 1996. Google ScholarDigital Library
- {HF95} J. Han, Y. Fu. Discovery of multiple-level association rules in relational databases, in Proc. 21st Int'l Conf. on Very Large Databases, pp. 420-431, 1995. Google ScholarDigital Library
- {HRU96} V. Harinarayan, A. Rajarman, J. D. Ullman. Implementing data cubes efficiently, in Proc. ACM SIGMOD Int'l. Conf. on Management of Data, pp. 205-216, 1996. Google ScholarDigital Library
- {KCGS93} W. Kim, I. Choi, S. K. Gala, M. Scheevel. On Resolving Schematic Heterogeneity in Multidatabase Systems, in Distributed and Parallel Databases 1(3): 251-279, 1993. Google ScholarDigital Library
- {KS98} V. Kashyap, A. Sheth. Semantic Heterogeneity in Global Information Systems: the Role of Metadata. Context and Ontology, in M.P. Papazoglou, G. Schlageter (eds). Cooperative Information Systems, pp. 139-178, 1998.Google Scholar
- {MBNG97} M. D. Mulvenna, A. G. Büchner, M. T. Norwood, C. Grant. The Soft-Push: Mining Internet Data for Marketing Intelligence, in Proc. Working Conf. on Electronic Commerce in the Framework of Mediterranean Countries Development, pp. 333-349, 1997.Google Scholar
- {MNB98} M. D. Mulvenna, M. T. Norwood, A. G. Büchner. Data-driven Marketing, in Int'l. Journal of Electronic Markets, 8(3) 32-35, 1998.Google ScholarCross Ref
- {MT96} H. Manilla, H. Toivonen. Discovering generalized episodes using minimal occurences, in Proc. 2nd Int'l. Conf. on Knowledge Discovery and Data Mining, pp. 146-151, 1996.Google Scholar
- {W3C98a} World Wide Web Consortium. http://www.w3.org /RDF/, 1998.Google Scholar
- {W3C98b} World Wide Web Consortium. http://www.w3.org /XML/, 1998.Google Scholar
- {YJGD96} T. Yan, M. Jacobsen, H. Garcia-Molina, U. Dayal, From User Access Patterns to dynamic Hypertext Linking, in 5th Int'l. WWW Conf., 1996. Google ScholarDigital Library
- {ZXH98} O. R. Zaïane, M. Xin, J. Han. Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs, in Proc. Advances in Digital Libraries Conf., pp. 19-29, 1998. Google ScholarDigital Library
Index Terms
- Discovering Internet marketing intelligence through online analytical web usage mining
Recommendations
Web usage mining: discovery and applications of usage patterns from Web data
Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern ...
Discovery of Interesting Association Rules Based on Web Usage Mining
MEDIACOM '10: Proceedings of the 2010 International Conference on Multimedia CommunicationsMining of association rules is an important research topic in web usage mining. The purpose of this paper is to research how to dig interesting association rules effectively from the Web logs after been preprocessed. Firstly, using the FP-growth ...
Web usage mining: extracting unexpected periods from web logs
Existing Web usage mining techniques are currently based on an arbitrary division of the data (e.g. "one log per month") or guided by presumed results (e.g. "what is the customers' behaviour for the period of Christmas purchases?"). These approaches ...
Comments