Date:
Thursday, October 13, 2011 - 13:00
Location: U of M - 575
Walter Instructor(s): Haoyu Yu, MSI, Dr. Michael S. Steinbach,
Weka is a freely available software package for knowledge discovery and data mining that contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
In this tutorial, we will go through the basic steps involved in loading data into Weka, exploring and filtering data, and applying Weka data mining tools. The emphasis will be on techniques for classification, such as decision trees, nearest neighbors, naive Bayes, and artificial neural networks. However, as time allows, we will briefly discuss other capabilities of Weka, such as clustering, association analysis, and variable selection.
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