Overview of Clustering Algorithms in Knowledge Discovery

Clustering is a crucial step in Knowledge Discovery in Databases (KDD), involving grouping entities based on similarities. It can be hierarchical or non-hierarchical, with methods like centroid, nearest neighbor, farthest neighbor, average linkage, and Ward's method. Hierarchical methods create tree-like structures, while non-hierarchical methods organize objects into clusters without a tree structure. Both approaches have their advantages and disadvantages in handling different types of data.

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