DDDM2007: Domain Driven Data Mining

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.

REFERENCES

• 1: Longbing Cao , Chengqi Zhang , Qiang Yang , David Bell , Michail Vlachos , Bahar Taneri , Eamonn Keogh , Philip S. Yu , Ning Zhong , Mafruz Zaman Ashrafi , David Taniar , Eugene Dubossarsky , Warwick Graco, Domain-Driven, Actionable Knowledge Discovery, IEEE Intelligent Systems, v.22 n.4, p.78-88, c3, July 2007 [doi>10.1109/MIS.2007.67]
• 2: 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.
• 3: Cao, L., Zhang, C. Domain-driven data mining, Advances in Data Warehousing and Mining, IGI Publisher, 2007.
• 4: Pedro Domingos, Toward knowledge-rich data mining, Data Mining and Knowledge Discovery, v.15 n.1, p.21-28, August 2007 [doi>10.1007/s10618-007-0069-7]
• 5: Usama M. Fayyad , Gregory Piatetsky-Shapiro , Ramasamy Uthurusamy, Summary from the KDD-03 panel: data mining: the next 10 years, ACM SIGKDD Explorations Newsletter, v.5 n.2, December 2003 [doi>10.1145/980972.981004]
• 6: F. Özden Gür Ali , William A. Wallace, Bridging the gap between business objectives and parameters of data mining algorithms, Decision Support Systems, v.21 n.1, p.3-15, Sept. 1997 [doi>10.1016/S0167-9236(97)00010-9]
• 7: Han, J. Towards Human-Centered, Constraint-Based, Multi-Dimensional Data Mining, An invited talk at Univ. Minnesota, Minneapolis, Minnesota, 1999.
• 8: Hans-Peter Kriegel , Karsten M. Borgwardt , Peer Kröger , Alexey Pryakhin , Matthias Schubert , Arthur Zimek, Future trends in data mining, Data Mining and Knowledge Discovery, v.15 n.1, p.87-97, August 2007 [doi>10.1007/s10618-007-0067-9]
• 9: Bing Liu , Wynne Hsu , Lai-Fun Mun , Hing-Yan Lee, Finding Interesting Patterns Using User Expectations, IEEE Transactions on Knowledge and Data Engineering, v.11 n.6, p.817-832, November 1999 [doi>10.1109/69.824588]
• 10: Gregory Piatetsky-Shapiro , Chabane Djeraba , Lise Getoor , Robert Grossman , Ronen Feldman , Mohammed Zaki, What are the grand challenges for data mining?: KDD-2006 panel report, ACM SIGKDD Explorations Newsletter, v.8 n.2, p.70-77, December 2006 [doi>10.1145/1233321.1233330]
• 11: Gregory Piatetsky-Shapiro, Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from "university" to "business" and "analytics", Data Mining and Knowledge Discovery, v.15 n.1, p.99-105, August 2007 [doi>10.1007/s10618-006-0058-2]
• 12: Pang-Ning Tan , Vipin Kumar , Jaideep Srivastava, Selecting the right interestingness measure for association patterns, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775053]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  A. General Literature
  A.1 INTRODUCTORY AND SURVEY