Longbing Cao
Yanchang Zhao
Philip S. Yu
Abstract
Real-world data mining generally must consider and involve domain and business oriented factors such as human knowledge, constraints and business expectations. This encourages the development of a domain driven methodology to strengthen data-centered pattern mining. This report presents a review of the ACM SIGKDD Workshop on Domain Driven Data Mining (DDDM2007), held in conjunction with the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD07), which was held in San Jose, USA on 12 August, 2007. The aims and objectives of this workshop were to provide a premier forum for sharing innovative findings, knowledge, insights, experiences and lessons in tackling challenges met in domain driven, actionable knowledge discovery in the real world.
- Cao, L., Zhang, C., Yu, P., et al. Domain-Driven actionable knowledge discovery, IEEE Intelligent Systems, 22(4): 78--89, 2007. Google Scholar
Digital Library
- Cao, L., Zhang, C. The evolution of KDD: Towards domain-driven data mining. Int. J. of Pattern Recognition and Artificial Intelligence, 21(4): 677--692, 2007.Google ScholarCross Ref
- Cao, L., Zhang, C. Domain-driven data mining, Advances in Data Warehousing and Mining, IGI Publisher, 2007.Google Scholar
- Domingos, P., Toward knowledge-rich data mining, Data Mining and Knowledge Discovery: An International Journal, 15(1): 21--28, 2007. Google ScholarDigital Library
- Fayyad, U., Shapiro G., Uthurusamy R., Summary from the KDD-03 panel Data mining: the next 10 years, ACM SIGKDD Explorations Newsletter, 5(2): 191--196, 2003. Google ScholarDigital Library
- Gur Ali, O. F., Wallace, W. A. Bridging the gap between business objectives and parameters of data mining algorithms, Decision Support Systems, 21:3--15, 1997. Google ScholarDigital Library
- Han, J. Towards Human-Centered, Constraint-Based, Multi-Dimensional Data Mining, An invited talk at Univ. Minnesota, Minneapolis, Minnesota, 1999.Google Scholar
- Kriegel, H., Borgwardt, K., Kroger, P., Pryakhin, A., Schubert, M. and Zimek, A. Future trends in data mining, Data Mining and Knowledge Discovery: An International Journal, 15(1): 87--97, 2007. Google ScholarDigital Library
- Liu, B., Hsu, W., Mun, L., and Lee, H. Finding Interesting Patterns Using User Expectations, IEEE Transactions on Knowledge and Data Engineering, 11(6): 817--832, 1999. Google ScholarDigital Library
- Shapiro, G-P., Djeraba, C., Getoor, L., Grossman, R., Feldman R., and Zaki M. What Are The Grand Challenges for Data Mining? KDD-2006 Panel Report, ACM SIGKDD Explorations Newsletter, 8(2): 70--77, 2006. Google ScholarDigital Library
- Shapiro, G. P., Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from "university" to "business" and "analytics", Data Mining and Knowledge Discovery: An International Journal, 15(1): 99--105, 2007. Google ScholarDigital Library
- Tan, P., Kumar, V., Srivastava, J. Selecting the Right Interestingness Measure for Association Patterns, Proceedings of SIGKDD02, 15(1): 32--41, 2002. Google ScholarDigital Library
Index Terms
- DDDM2007: Domain Driven Data Mining
Comments