Introduction do Data Mining

Abstract

Cluster analysis divides data into groups (clusters) that aremeaningful, useful, or both. If meaningful groups are the goal, then the clusters should capture the natural structure of the data. In some cases, however, cluster analysis is only a useful starting point for other purposes, such as data summarization. Whether for understanding or utility, cluster analysis has long played an important role in a wide variety of fields: psychology and other social sciences, biology, statistics, pattern recognition, information retrieval, machine learning, and data mining. There have been many applications of cluster analysis to practical prob- lems. We provide some specific examples, organized by whether the purpose of the clustering is understanding or utility.