Data mining, sometimes called knowledge discovery, is the process of sifting large volumes of data for correlations, patterns, and trends.

Same as CSCA 5512Same as CSCA 5512 Specialization: Data Mining Foundations and Practice Instructor: Dr. Qin (Christine) Lv, Associate Professor of Computer Science Prior knowledge needed: Familiarity ...

Effective data mining techniques are essential to using data for competitive advantage.

Course topics include data exploration and preparation, sampling techniques, model evaluation methods, classification, regression, clustering, association rule mining, and text mining.

Courses Data Mining Pipeline Data Mining Methods Data Mining Projects This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees ...

Data mining and knowledge discovery represent an integrative process through which large, complex and heterogeneous datasets are transformed into actionable insights.

There are many facets of data mining, which includes using information for a database for anything from increasing a business's revenue to developing better healthcare infrastructures. (Kaleena ...

Chris Chatfield, Model Uncertainty, Data Mining and Statistical Inference, Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 158, No. 3 (1995), pp. 419-466 ...

Y. Liu, M. Schumann, Data Mining Feature Selection for Credit Scoring Models, The Journal of the Operational Research Society, Vol. 56, No. 9 (Sep., 2005), pp. 1099-1108 ...

Regression: Data mining can be used to construct predictive models based on many variables. Facebook, for example, might be interested in predicting future engagement for a user based on past ...