Machine Learning - Clustering

The goal of clustering a set of data is to

• divide them into groups of data that are near each other

• choose the best data from the set

• determine the nearest neighbors of each of the data

• predict the class of data

Which of the following statements about the K-means algorithm are correct?

• The K-means algorithm is sensitive to outliers.

• For different initializations, the K-means algorithm will definitely give the same clustering results.

• The centroids in the K-means algorithm may not be any observed data points.

• The K-means algorithm can detect non-globular clusters.

What are the two types of Hierarchical Clustering

• Top-Down Clustering (Divisive)

• Dendrogram

• K-means

• Bottom-Top Clustering (Agglomerative)

What is a Dendrogram?

• A tree diagram used to illustrate the arrangement of clusters in partitional clustering.

• A tree diagram used to illustrate the arrangement of clusters in hierarchical clustering.

• A type of hierarchical clustering.

• A type of bar chart diagram to visualize k-means clusters.

Which of the following is required by K-means clustering?

• defined distance metric

• initial guess as to cluster centroids

• number of clusters

• all answers are correct

In the figure above, if you draw a horizontal line on y-axis for y=2. What will be the number of clusters formed?

• 2

• 3

• 4

• 5

Considering the K-median algorithm, if points (0, 3), (2, 1), and (-2, 2) are the only points which are assigned to the first cluster now, what is the new centroid for this cluster?

• (2,1)

• (2,0)

• (1,2)

• (0,2)

How to Define Inter-Cluster Distance in Hierarchical Clustering?

• Min

• Max

• Centroid

• Average

Metrics for evaluate clustering results, except?

• Innertia

• Silhouette Score

• Dunn Index

• Accuracy

What is the distance between data in the same cluster called?

• Intra Cluster Distance

• Inter Cluster Distance

• Euclidean Distance

• Manhatan Distance