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Datamining

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Data Mining, Predictive Modeling, Techniques. Data Mining Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related - also known as "big data") in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data.

Data Mining, Predictive Modeling, Techniques

The ultimate goal of data mining is prediction - and predictive data mining is the most common type of data mining and one that has the most direct business applications. The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identification with validation/verification, and (3) deployment (i.e., the application of the model to new data in order to generate predictions). Stage 1: Exploration. Statistical Data Mining Tutorials. The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms.

Statistical Data Mining Tutorials

These include classification algorithms such as decision trees, neural nets, Bayesian classifiers, Support Vector Machines and cased-based (aka non-parametric) learning. They include regression algorithms such as multivariate polynomial regression, MARS, Locally Weighted Regression, GMDH and neural nets. And they include other data mining operations such as clustering (mixture models, k-means and hierarchical), Bayesian networks and Reinforcement Learning. I hope they're useful (and please let me know if they are, or if you have suggestions or error-corrections). Click here for a short list of topics. Data Mining - ApontamentosGI. Tutorial (1): A simple decision tree.