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.

08 May 2025
Answer :
Word Count : 549

Clustering is an essential technique in data mining and data warehousing, often used to group similar data points together. There are various clustering techniques, each with its own strengths and weaknesses. Three widely used clustering techniques are K-means, hierarchical clustering, and DBSCAN.

K-means Clustering is a partition-based clustering algorithm where the data is divided into 'K' clusters. The algorithm works by first selecting K centroids, then assigning each data point to the nearest centroid. After the assignment, the centroid is recalculated as the mean of the points in the cluster, and the process is repeated until convergence.

  • Advantages: K-means is computationally efficient and works well for large datasets. It is easy to implement and widely used in practice.

  • Limitations: The algorithm requires the number of clusters (K) to be pre-defined, which is not _______ _____ ________ _____ _______ ________ _________ _______ ______.
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