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Clustering ensembles have emerged as a powerful method for improving both the robustness as well as the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial, or statistical perspectives. This study extends previous(More)
This paper summarizes work on an approach that combines feature selection and data classiication using Genetic Algorithms. First, it describes our use of Genetic Algorithms combined with a K-nearest neighbor algorithm to optimize classiication by searching for an optimal feature weight-ing, essentially warping the feature space to coalesce individuals(More)
Web-based educational technologies allow educators to study how students learn (descriptive studies) and which learning strategies are most effective (causal/predictive studies). Since web-based educational systems collect vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships(More)
If you change the CS1 language to Python, what is the impact on the rest of the curriculum? In earlier work we examined the impact of changing CS1 from C++ to Python while leaving CS2 in C++. We found that Python-prepared CS1 students fared no differently in CS2 than students whose CS1 course was in C++, even though CS2 was taught in C++ and covered the(More)
This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize(More)