Fundamentals of Data Mining Assignment

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Reference no: EM132517989

Fundamentals of Data Mining Assignment -

Q1. Describe the similarities and difference between Decision and Regression Tree learning.

Q2. Use the Regression tree learning scheme (M5P) to analyze the CPU.arff. Evaluate the difference between a model tree and a regression tree (right click on the option provides "build Regression Tree" option). Experiment with the available parameters to understand their significance and discuss how they influence the model?

Q3. Use M5P Model tree learning scheme (M5P, ensure that the "Build Regression Tree" parameter =False) to analyze the bolts data (bolts.arff without the TIME attribute):

Analyze the data. What adjustments have the greatest effect on the time to count 20 bolts?

How does this model differ from the Regression Tree induced tree?

Q4. Use a k-means clustering (SimpleKMeans) technique to analyze the iris data set. What did you set the k value to be? Try several different values. What was the random seed value? Experiment with different random seed values. How does changing of these values influence the produced model? Use different distance functions. Did they produce significantly different clustering models?

Note - Using weka for data mining. Let me know what datasets you need for me to upload.

Reference no: EM132517989

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