This document summarizes and analyzes various clustering algorithms used in data mining. It begins with an introduction to clustering and discusses hierarchical and partitional clustering approaches. Specific algorithms discussed include k-means, single linkage, and fuzzy clustering. The document compares techniques on aspects like complexity and sensitivity. It concludes that choosing the best clustering algorithm depends on factors like the data type, size, and desired cluster structure. The ideal is to have criteria to automatically select the appropriate algorithm for a given data set.