Data mining labs. Spring 2009. University of Victoria, Canada
Presentations, sample data sources and python implementations of various data mining algorithms which were prepared and used for data mining tutorials in data mining course. Lab 1. Decision trees. Presentation: Lab1_decisiontree.pdf Sample data sets: data.zip Code starters: code.zip Solutions: solutions.zip Lab 2. Using map-reduce framework to compute Attribute-Value-Class sets for decision trees on massive data. Presentation: Lab2AVCSetsClusterPython.pdf Sample data sets: data.zip Code starters: code.zip Solutions: solutions.zip Lab 3. Classifiers tutorial: decision trees, association rules and naïve Bayes. Classifiers with WEKA. Presentations: Lab3_assrulesexample.pdf, Lab3_decisiontreeexample.pdf, Lab3_naivebayesxample.pdf, Lab3_classifiersWithWEKA.pdf Sample data sets: data.zip Lab 4. Bayesian networks. Presentation: Lab4_Bayesiannetworks.pdf Sample data set: weather.nominal.arff Lab5. ROC curves with WEKA. Presentation: Lab5_ROC_weka.pdf Sample data set (zipped): adult_income.zip, conversionsofeducation.txt Lab 6. Optimizations with genetic algorithm. Presentation: Lab6_genetic_algorithm.pdf Sample data (to download): schedule.txt Code starters: code.zip Solutions: solutions.zip Lab 7. Frequent itemsets tutorial: apriori and FP-trees. Presentations: Lab7_apriori.pdf, Lab7_fpree.pdf Sample data sets: data.zip Code starters (includes fimi06b): code.zip Solutions: solutions.zip Lab 8. Clustering tutorial. Presentation: Lab8_distances_clustering.pdf Sample data sets: data.zip Code for demonstration: code.zip Lab 9. WEB ranking tutorial: link analysis and LSA Presentation: Lab9_rankingWEB.pdf Code for demonstration: PageRankCode