Abstract: Clustering is an unsupervised classification of the cluster pattern observations in the group of data elements or eigenvectors. The caking problem is solved in many cases and in many disciplines by researchers. This reflects its broad appeal and practicality as a step in exploratory data analysis. However, grouping is a difficult combination of assumptions in different communities and contextual differences have made useful general concepts and methods of slow turn. This article introduces the basic concepts of statistical pattern recognition and community access to a wider community of professionals, providing an overview of the methods of grouping patterns of useful tips and reference targets. We propose clustering techniques for categorizing and identifying cross-problems and recent developments. Some important applications of clustering algorithms such as image segmentation target recognition and information retrieval are also described.
Keywords: Clustering Algorithm, Data Analysis, Feature Selection, Feature Extraction.