A survey on clustering in data mining

Abstract

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. Unsupervised learning (clustering) deals with which have not been pre classified in any way and so do not have a class attribute associated with them. The scope of applying clustering algorithm is to discover useful but unknown classes of items. Unsupervised learning is an approach of learning where instances are automatically placed into meaningful groups based on their similarity. This paper addresses fundamental concepts of unsupervised learning while it serveys recent clustering algorithm and their complexities.