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When you think about it, almost every device or service we use generates a large amount of data (for example, Facebook processes approximately 500+ terabytes of data per day).
This weeks guest post comes from KDD (KnowledgeDiscovery and DataMining). Every year they host an excellent and influential conference focusing on many areas of data science. Honestly, KDD has been promoting data science way before data science was even cool. 1989 to be exact.
This data alone does not make any sense unless it’s identified to be related in some pattern. Datamining is the process of discovering these patterns among the data and is therefore also known as KnowledgeDiscovery from Data (KDD). DataMining Process. DataMining Models.
ACM SIGKDD Invites Industry and Academic Experts to Submit Advancements in DataMining, KnowledgeDiscovery and Machine Learning for 26 th Annual Conference in San Diego.
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 DataMining, 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 DataMining (KDD), 2013. [3] 3] Bradley Efron, "Robbins, Empirical Bayes, and Microarrays" , Technical Report, 2003. [4]
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