This document discusses clustering as a basic data mining task. It defines clustering as the process of grouping similar abstract objects together. The main advantages of clustering are that it can adapt to changes in data and identify distinguishing features between groups. Some applications of clustering mentioned include market research, biology, image processing, and outlier detection. The document also lists requirements for clustering algorithms in data mining like scalability, handling different data types, and dealing with noisy data. Finally, it briefly describes several clustering methods like partitioning, hierarchical, density-based, and model-based clustering.