Two approaches to clustering discussed in the text are
A) k-means clustering and hierarchical clustering
B) intuitive clustering and methodical clustering
C) MIPS clustering and pixel clustering
D) centroid clustering and VMI

Question

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These two approaches are the most widely used and effective clustering methods in data science. K-means clustering is a method that divides a set of data points into k clusters based on their similarity, and it is particularly useful for large datasets. Hierarchical clustering is a method that creates a tree-based representation of data points that groups them together based on similarity, and it is particularly useful for smaller datasets. Using a combination of these two methods can provide better insights into the data and more accurate clustering results.

Two Approaches to Clustering Discussed in the Text Are