A framework of business intelligence-driven data mining for e-Business

NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC

Volumn , Issue , 2009, Pages 1964-1970

  Hang, Yang ^a   Fong, Simon ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

BI driven Data Mining; Business intelligence; Data mining

Indexed keywords

BI-DRIVEN DATA MINING; BUSINESS INTELLIGENCE; CONSTRUCTION PROCESS; DATA MINING METHODS; DATA MINING PROCESS; EBUSINESS;

ELECTRONIC COMMERCE; INFORMATION MANAGEMENT; MINING; SEMICONDUCTOR STORAGE;

DATA MINING;

EID: 73549104824     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NCM.2009.403     Document Type: Conference Paper

References (22)

1. H. P. Luhn. "A Business Intelligence System" (PDF). IBM Journal,October 1958.
2. Simoudis, E., Reality check for data mining, IEEE Expert: Intelligent Systems and Their Applications , Volume 11 Issue 5, pp 26-33, Oct. 1996.
3. Spahn, M., Kleb, J., Grimm, S., and Scheidl, S. Supporting business intelligence by providing ontology-based end-user information selfservice. In Proceedings of the First international Workshop on ontology-Supported Business intelligence (OBI '08), vol. 308. ACM, New York, NY, pp. 1-12, 2008.
4. K. J. Cios, W. Pedrycz, and R. M. Swiniarski. Data Mining: A Knowledge Discovery Approach., Chapter 2, pp. 9-24, Springer Press, 2007.
5. Zimmermann, H., OSI Reference Model-The ISO Model of Architecture for Open Systems Interconnection, IEEE Transactions on Communications, Volume 28, Issue 4, pp 425- 432, 1980
6. Fu Y., Distributed Data Mining: An overview. Newsletter of the IEEE Technical Committee on Distributed Processing, pp 5-9, Spring, 2001.
7. Mu-Chun Su, M-C; and Chou, C-H. A Modified Version of the KMeans Algorithm with a Distance Based on Cluster Symmetry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, pp 674-680, 2001.
8. Breiman, L. Random Forests. Machine Learning Journal,45, pp 5-32, 2001.
9. Graco, W., Semenova, T., and Dubossarsky, E. Toward knowledgedriven data mining. In Proceedings of the 2007 international Workshop on Domain Driven Data Mining. DDDM '07. ACM, New York, NY, pp 49-54. 2007.
10. S. Chakrabarti, "Data mining for hypertext: A tutorial survey." SIGKDD: SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery and Data Mining, ACM, Vol. 1, No. 2, pp. 1-11, 2000
11. Tibor S., Iveta Z., Lenka L., "Distributed data mining and data warehouse", XXX.ASR 2005 Seminar, Instruments and Control,pp 417-420, April.2005
12. Tian Chen; Zhi-geng Pan; Jian-ming Zheng, "EasyMall - An Interactive Virtual Shopping System," Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on , vol.4, no., pp.669-673, 18-20 Oct. 2008
13. Renata Ivancsy, Istvan Vajk, Frequent Pattern Mining in Web Log, 2006 Acta Polytechnica Hungarica, Vol 3, No.1, pp 77-90, 2006.
14. B. Pinnington, B. L. B, and E. Ferneley. Too Much of a Good Thing? A Field Study of Challenges in Business Intelligence Enabled Enterprise System Environments. In Proceedings of the 15th European Conference on Information Systems, pages 1941-1952, 2007.
15. V. Stefanov and B. List. Explaining Data Warehouse Data to Business Users - A Model-Based Approach to Business Metadata. In Proceedings of the 15th European Conference on Information Systems, pages 2062-2073, 2007.
16. Breton E. and Bézivin J. Weaving Definition and Execution Aspects of Process Meta-models. In 35th Hawaii Int. Conf. on System Sciences, pp 290, 2002
17. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: an Introduction to Cluster Analysis, John Wiley & Sons, 1990.
18. Qingzhang Chen; Jianghong Han; Yuqing Chu; Xiaodong Ying, "Mining Consumers' Most Adaptive Products by Efficient Clustering Algorithm," Artificial Reality and Telexistence--Workshops, 2006. ICAT '06. 16th International Conference on , vol., no., pp.195-199, Nov. 29 2006-Dec. 1 2006.
19. S. Kotsiantis, D. Kanellopoulos, Association Rules Mining: A Recent Overview, GESTS International Transactions on Computer Science and Engineering, Vol.32, Issue 1, pp. 71-82, 2006.
20. Han, J. and Pei, J. Mining frequent patterns by pattern-growth: methodology and implications. ACM SIGKDD Explorations Newsletter 2, 2, pp.14-20, 2000.
21. Pei, J., Han, J., Mortazavi-Asl, B., and Zhu, H. Mining Access Patterns Efficiently from Web Logs. In Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications (April 18 - 20, 2000). T. Terano, H. Liu, and A. L. Chen, Eds. Lecture Notes In Computer Science, vol. 1805. Springer-Verlag, London, pp.396-407,2000.
22. Domingos, P. and Pazzani, M. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Mach. Learn. 29, 2-3, pp.103-130, 1997.