Given the training set D in Table 1, apply the decision tree induction algorithm for classification to construct the decision tree to predict the class label (Life Insurance Promotion (yes/no)). Explain the steps you have taken to construct the decision tree in detail. Describe each of the following clustering algorithms in terms of the following criteria: (1) shapes of cluster that can be determined; (2) input parameters that must be specified; and (3) limitations and advantages. 1. K-means 2. K-medoids 3. Agglomerative Clustering 4. DBSCAN Given the training set D in Table 1, apply the decision tree induction algorithm for classification to construct the decision tree to predict the class label (Life Insurance Promotion (yes/no)). Explain the steps you have taken to construct the decision tree in detail. Describe each of the following clustering algorithms in terms of the following criteria: (1) shapes of cluster that can be determined; (2) input parameters that must be specified; and (3) limitations and advantages. 1. K-means 2. K-medoids 3. Agglomerative Clustering 4. DBSCAN