IGNOU MCS 221 2026 2027 - Differentiate partitioning, hierarchical and density-based c

Question

Differentiate partitioning, hierarchical and density-based clustering techniques. Discuss the working of K-means and DBSCAN and compare their suitability for real-world applications involving noisy data.

30 Jul 2026

Answer :

Word Count : 629

Clustering is an important data mining technique used to group similar data objects into meaningful clusters without prior knowledge of class labels. Different clustering techniques are applied depending on the structure of data, size of datasets, and the presence of noise. Partitioning, hierarchical, and density-based clustering are three major categories of clustering methods used in data warehousing and data mining. Partitioning clustering techniques divide a dataset into a predefined number of non-overlapping clusters. Each data object belongs to exactly one cluster, and the objective is to maximize similarity among objects within the same cluster while minimizing similarity between different clusters. These techniques require the number of clusters to be specified in advance. They are efficient for large datasets but are sensitive to initial conditions and outliers. K-means is the most widely used partitioning clustering algorithm. Hierarchical clustering creates a tree-like structure called a dendrogram that represents relationships among data objects. It

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