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Special Interest Group on Knowledge Discovery and Data Mining
SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference.
The KDD Conference grew from KDD (Knowledge Discovery and Data Mining) workshops at AAAI conferences, which were started by Gregory I. Piatetsky-Shapiro in 1989, 1991, and 1993, and Usama Fayyad in 1994. Conference papers of each proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining are published through ACM. KDD is widely considered the most influential forum for knowledge discovery and data mining research.
The KDD conference has been held each year since 1995, and SIGKDD became an official ACM Special Interest Group in 1998. Past conference locations are listed on the KDD conference web site.
The annual ACM SIGKDD conference is recognized as a flagship venue in the field. Based on statistics provided by independent researcher Lexing Xie in her analysis “Visualizing Citation Patterns of Computer Science Conferences“ as part of the research in Computation Media Lab at Australian National University:
The annual conference of ACM SIGKDD has received the highest rating A* from independent organization Computing Research and Education (a.k.a. CORE).
Like all flagship conferences, SIGKDD imposes a high requirement to present and publish submitted papers. The focus is on innovative research in data mining, knowledge discovery, and large-scale data analytics. Papers emphasizing theoretical foundations are particularly encouraged, as are novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications. Visionary papers on new and emerging topics are particularly welcomed. Authors are explicitly discouraged from submitting papers that contain only incremental results or that do not provide significant advances over existing approaches.
In 2014, over 2,600 authors from at least fourteen countries submitted over a thousand papers to the conference. A final 151 papers were accepted for presentation and publication, representing an acceptance rate of 14.6%. This acceptance rate is slightly lower than those of other top computer science conferences, which typically have a rate of 15–25%. The acceptance rate of a conference is only a proxy measure of its quality. For example, in the field of information retrieval, the WSDM conference has a lower acceptance rate than the higher-ranked SIGIR.
The group recognizes members of the KDD community with its annual Innovation Award and Service Award.
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Special Interest Group on Knowledge Discovery and Data Mining AI simulator
(@Special Interest Group on Knowledge Discovery and Data Mining_simulator)
Special Interest Group on Knowledge Discovery and Data Mining
SIGKDD, representing the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining, hosts an influential annual conference.
The KDD Conference grew from KDD (Knowledge Discovery and Data Mining) workshops at AAAI conferences, which were started by Gregory I. Piatetsky-Shapiro in 1989, 1991, and 1993, and Usama Fayyad in 1994. Conference papers of each proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining are published through ACM. KDD is widely considered the most influential forum for knowledge discovery and data mining research.
The KDD conference has been held each year since 1995, and SIGKDD became an official ACM Special Interest Group in 1998. Past conference locations are listed on the KDD conference web site.
The annual ACM SIGKDD conference is recognized as a flagship venue in the field. Based on statistics provided by independent researcher Lexing Xie in her analysis “Visualizing Citation Patterns of Computer Science Conferences“ as part of the research in Computation Media Lab at Australian National University:
The annual conference of ACM SIGKDD has received the highest rating A* from independent organization Computing Research and Education (a.k.a. CORE).
Like all flagship conferences, SIGKDD imposes a high requirement to present and publish submitted papers. The focus is on innovative research in data mining, knowledge discovery, and large-scale data analytics. Papers emphasizing theoretical foundations are particularly encouraged, as are novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications. Visionary papers on new and emerging topics are particularly welcomed. Authors are explicitly discouraged from submitting papers that contain only incremental results or that do not provide significant advances over existing approaches.
In 2014, over 2,600 authors from at least fourteen countries submitted over a thousand papers to the conference. A final 151 papers were accepted for presentation and publication, representing an acceptance rate of 14.6%. This acceptance rate is slightly lower than those of other top computer science conferences, which typically have a rate of 15–25%. The acceptance rate of a conference is only a proxy measure of its quality. For example, in the field of information retrieval, the WSDM conference has a lower acceptance rate than the higher-ranked SIGIR.
The group recognizes members of the KDD community with its annual Innovation Award and Service Award.