MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples la-bels. On the other hand, new algorithms must be applied to merge sub-clusters at leaf nodes into actual clusters.

Detailed explanation-2: -Q3. Can decision trees be used for performing clustering? Decision trees can also be used to for clusters in the data but clustering often generates natural clusters and is not dependent on any objective function.

Detailed explanation-3: -Decision trees are a method for classifying subjects into known groups. They’re a form of supervised learning. The clustering algorithms can be further classified into “eager learners, ” as they first build a classification model on the training data set and then actually classify the test dataset.

Detailed explanation-4: -Hence, the K-nearest neighbor method is not a clustering method.

Detailed explanation-5: -k-means is the most widely-used centroid-based clustering algorithm. Centroid-based algorithms are efficient but sensitive to initial conditions and outliers. This course focuses on k-means because it is an efficient, effective, and simple clustering algorithm.