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Introduction to Data Mining for Educational Researchers

The goal of this tutorial is to share data mining tools and techniques used by computer scientists with educational social scientists We broadly define educational social scientists as being made up of people with backgrounds in the learning sciences, cognitive psychology, and educational research The learning analytics community is heavily populated with researchers of these backgrounds .

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Introduction to Data Mining - University of Minnesota

each outcome from the data, then this is more like the problems considered by data mining However, in this specific case, solu-tions to this problem were developed by mathematicians a long time ago, and thus, we wouldn’t consider it to be data mining (f) Predicting the future stock price of a company using historical records Y

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The following slides are based on the additional material provided with the textbook that we use and the book by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar "Introduction to Data Mining" Sep 05, 2007: Course Overview Sep 10, 2007: Data Warehouses and OLAP Sep 12, 2007: OLAP II Sep 17, 2007: Data Preprocessing

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DATA MINING LECTURE 2

Data Preprocessing Data Mining Result Post-processing Data Collection •Preprocessing: Real data is noisy, incomplete and inconsistent Data cleaning is required to make sense of the data • Techniques: Sampling, Dimensionality Reduction, Feature selection •The preprocessing step determines the input to the data mining algorithm

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