Unsupervised Learning

What is the main objective of the K-means clustering algorithm?

• Maximizing intra-cluster similarity

• Minimizing intra-cluster variance

• Maximizing inter-cluster similarity

• Minimizing inter-cluster distance

Tags

CCSS.6.SP.A.3

How is the initial centroid position determined in K-means clustering?

• Randomly

• Based on the mean of all data points

• Based on the farthest data points from each other

• Based on the median of all data points

What does DBSCAN stand for?

• Density-Based Spatial Clustering of Applications with Noise

• Distance-Based Similarity Clustering with Noise

• Deterministic Boundary Search for Clustering with Noise

• Dynamic Binary Splitting for Cluster Analysis with Noise

What are the two main parameters in DBSCAN?

• K and epsilon

• K and MinPts

• MinPts and epsilon

• Epsilon and radius

What is the significance of the epsilon parameter in DBSCAN?

• It determines the minimum number of points required to form a cluster

• It defines the maximum distance between points in the same cluster

• It sets the maximum number of iterations for the algorithm

• It specifies the size of the neighborhood for density estimation

What is the output of hierarchical clustering?

• Centroids of clusters

• Labels of clusters

• Dendrogram

• Silhouette scores

What is the difference between agglomerative and divisive hierarchical clustering?

• Agglomerative is faster but less accurate than divisive

• Agglomerative requires the number of clusters as input, while divisive does not

• Agglomerative starts with individual data points, while divisive starts with one cluster containing all data points

• Agglomerative merges clusters, while divisive splits clusters

What is the main goal of Principal Component Analysis (PCA)?

• Clustering data points

• Reducing the dimensionality of data

• Finding outliers in the dataset

• Classifying data points into categories

What does PCA seek to maximize in the data?

• Variance

• Covariance

• Entropy

• Mean

Tags

CCSS.HSS.ID.B.5

How are principal components computed in PCA?

• By calculating the mean of each feature in the dataset

• By applying a kernel function to the data

• By finding eigenvectors of the covariance matrix of the data

• By performing gradient descent on the data