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Data mining overview : Data mining uses a combination of an explicit knowledge base, sophisticated analytical skills, and domain knowledge to uncover hidden trends and patterns. These trends and patterns form the basis of predictive models that enable analysts to produce new observations from existing data. Data mining models and algorithms : Models house the steps, modules, and resources of the data mining process. Some data mining models include the entire process for a particular purpose, be it to cluster or predict. A model is, however, different from an algorithm. An algorithm is a specific, mathematically driven data mining function, such as a neural network, classification and regression tree (C&RT), or K-means. Frequently used algorithms Beyond those mentioned in this paper, there are the genetic, market basket analysis, Kohonen network, link analysis, time/sequence, and text mining algorithms, to name just a few. Supervised and unsupervised modeling, Data mining applications in higher education.
Tags : market basket analysis, neural network classification, regression tree, hidden trends, predictive models, driven data, education content, explicit knowledge, analysis time, trends and patterns, time sequence, domain knowledge, text mining, data mining, analytical skills
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