Which of the following method is used to find the optimal features for cluster analysis

Related MCQs

• Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
• Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features
•            dataset with many features contains information proportional to the independence of all features and their variance.
• ______dataset with many features contains information proportional to the independence of all features and their variance.
• 8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0),(2,5)} C3: {(5,5), (9,9)} What will be the cluster centroids if you want to proceed for second iteration?
• The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N � the number of features) that distinctly classifies the data points.
• The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.
• What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches?
• We can also compute the coefficient of linear regression with the help of an analytical method called Normal Equation. Which of the following is/are true about Normal Equation? 1. We don't have to choose the learning rate 2. It becomes slow when number of features is very large 3. No need to iterate
• We can also compute the coefficient of linear regression with the help of an analytical method called �Normal Equation�. Which of the following is/are true about �Normal Equation�?1. We don�t have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate