These exercises are to be found in: Introduction to Data Mining, 2nd
Edition by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar.
1.1 Chapter 7
Exercises: 4,7,11,16,17,21,22
2 Practicum Problems
Prof. Panchal:
2.1 Problem 1
Load the auto-mpg sample dataset from the UCI Machine Learning Repository (auto-mpg.data) into Python using a Pandas dataframe. Using only the continuous fields as features, impute any missing values with the mean, and perform a Hierarchical Clustering (Use sklearn.cluster.AgglomerativeClustering) with linkage set to average and the default affinity set to a euclidean. Set the remaining parameters to obtain a shallow tree with 3 clusters as the target. Obtain the mean and variance values for each cluster, and compare these values to the values obtained for each class if we used origin as a class label. Is there a clear relationship between cluster assignment and class label?
2.2 Problem 2
Load the Boston dataset (sklearn.datasets.load boston()) into Python using a Pandas dataframe. Perform a K-Means analysis on scaled data, with the number of clusters ranging from 2 to 6. Provide the Silhouette score to justify which value of k is optimal. Calculate the mean values for all features in each cluster for the optimal clustering - how do these values differ from the centroid coordinates?
2.3 Problem 3
Prof. Panchal:
Load the wine dataset (sklearn.datasets.load wine()) into Python using a Pandas dataframe. Perform a K-Means analysis on scaled data, with the number of clusters set to 3. Given the actual class labels, calculate the Homogeneity/Completeness for the optimal k - what information do each of these metrics provide?
