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This weeks guest post comes from KDD (KnowledgeDiscovery and Data Mining). Every year they host an excellent and influential conference focusing on many areas of datascience. Honestly, KDD has been promoting datascience way before datascience was even cool. 22-27, 2020.
ACM SIGKDD Invites Industry and Academic Experts to Submit Advancements in Data Mining, KnowledgeDiscovery and Machine Learning for 26 th Annual Conference in San Diego.
This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as KnowledgeDiscovery from Data (KDD).
Recently, we presented some basic insights from our effort to measure and predict long-term effects at KDD 2015 [1]. References [1] Henning Hohnhold, Deirdre O'Brien, Diane Tang, Focus on the Long-Term: It's better for Users and Business , Proceedings 21st Conference on KnowledgeDiscovery and Data Mining, 2015. [2]
Brendan McMahan et al, "Ad Click Prediction: a View from the Trenches" , Proceedings of the 19th ACM SIGKDD International Conference on KnowledgeDiscovery and Data Mining (KDD), 2013. [3] 3] Bradley Efron, "Robbins, Empirical Bayes, and Microarrays" , Technical Report, 2003. [4]
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