DDDM2007: Domain Driven Data Mining

Authors:
Longbing Cao;Chengqi Zhang;Yanchang Zhao;Philip S. Yu;Graham Williams
Affiliations:
University of Technology Sydney, Australia;University of Technology Sydney, Australia;University of Technology Sydney, Australia;IBM T.J. Watson Research center;Australian Taxation Office, Australia
Venue:
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Year:
2007

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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.