Question

Evaluate the different clustering techniques, including K-means, hierarchical clustering and DBSCAN. Explain the underlying principles of each technique, and discuss their advantages, limitations, and practical applications.

25 Jul 2024
Answer :
Word Count : 651

Clustering is a crucial technique in data mining and data warehousing, where the goal is to group a set of data points into clusters based on similarity. Different clustering techniques, such as K-means, hierarchical clustering, and DBSCAN, have unique methods, advantages, limitations, and practical applications.

K-means Clustering: K-means is one of the most popular clustering algorithms that divides a dataset into K distinct, non-overlapping clusters. The algorithm begins by randomly selecting K centroids, then assigns each data point to the nearest centroid. Afterward, the centroids are updated by calculating the mean of the points in each cluster. This process iterates until convergence.

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