[TRUE / FALSE] K-means clustering algorithms can find clusters of arbitrary shape. [TRUE / FALSE] The silhouette coefficient is a method to determine the natural number of clusters for partitioning algorithms. [TRUE / FALSE] DBSCAN clustering algorithm optimize an objective function. [TRUE / FALSE] A common weakness of association rule mining is that it produces too many frequent itemsets. [TRUE / FALSE] Lift corrects for high confidence in rule X-> Y when item X is bought regularly by customers. [TRUE / FALSE] A low support of X will increase the confidence of X -> Y. [TRUE / FALSE] When we have X -> Y and Y -> X then support of X is equal to support of Y. [TRUE / FALSE] In general, agglomerative clustering is slower than quadratic. [TRUE / FALSE] The best centroid for minimizing the SSE of a cluster is the mean of the points in the cluster. [TRUE / FALSE] Apriori principle indicates if an itemset is infrequent, then all of it subsets must also be infrequent. [TRUE / FALSE] Sparsification step in graph-based Chameleon algorithm can significantly reduce the effects of noise and outliers. [TRUE / FALSE] K Nearest Neighbor is considered as a non-parametric method. [TRUE / FALSE] Artificial neural network is guaranteed to be successfully trained using gradient descent for global optimal. [TRUE / FALSE] Boosting method produces an ensemble of classifiers through random sampling the training data set with replacement.
[TRUE / FALSE] K-means clustering algorithms can find clusters of arbitrary shape.
[TRUE / FALSE] The silhouette coefficient is a method to determine the natural number of clusters for partitioning algorithms.
[TRUE / FALSE] DBSCAN clustering
[TRUE / FALSE] A common weakness of association rule mining is that it produces too many frequent itemsets.
[TRUE / FALSE] Lift corrects for high confidence in rule X-> Y when item X is bought regularly by customers.
[TRUE / FALSE] A low support of X will increase the confidence of X -> Y.
[TRUE / FALSE] When we have X -> Y and Y -> X then support of X is equal to support of Y.
[TRUE / FALSE] In general, agglomerative clustering is slower than quadratic.
[TRUE / FALSE] The best centroid for minimizing the SSE of a cluster is the mean of the points in the cluster.
[TRUE / FALSE] Apriori principle indicates if an itemset is infrequent, then all of it subsets must also be infrequent.
[TRUE / FALSE] Sparsification step in graph-based Chameleon algorithm can significantly reduce the effects of noise and outliers.
[TRUE / FALSE] K Nearest Neighbor is considered as a non-parametric method.
[TRUE / FALSE] Artificial neural network is guaranteed to be successfully trained using gradient descent for global optimal.
[TRUE / FALSE] Boosting method produces an ensemble of classifiers through random sampling the training data set with replacement.
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