A. DBSCAN works very well even if the dataset has clusters with varying densities.B. Density based clustering, e.g., DBS

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A. DBSCAN works very well even if the dataset has clusters with varying densities.B. Density based clustering, e.g., DBSCAN is less susceptible to noise.C. Support Vector Machine is a lazy learner.
A. DBSCAN works very well even if the dataset has clusters with varying densities.B. Density based clustering, e.g., DBSCAN is less susceptible to noise.
A. DBSCAN works very well even if the dataset has clusters with varying densities.
B. Density based clustering, e.g., DBSCAN is less susceptible to noise.
C. Support Vector Machine is a lazy learner.
D. The gain for a split on an attribute of a training dataset for a decision tree modeling is always positive irrespective of the type of impurity measure.