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.

19 Apr 2025
Answer :
Word Count : 402

Clustering is a key technique in data mining used to group similar data points into clusters without predefined labels. It plays a significant role in data warehousing by enabling the discovery of patterns and relationships in large datasets. Among various clustering techniques, K-means, hierarchical clustering, and DBSCAN are widely used.

K-means clustering is a partition-based method where the number of clusters (K) is predefined. The algorithm randomly selects K centroids and assigns each data point to the nearest centroid, forming __________ _____ ____ ___ _______.
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