A Survey of Data Clustering Methods

Abstract

Data clustering is one of the most essential, common and interesting task to classification of patterns in different areas such as data mining, pattern recognition, artificial intelligence and etc. The objective of data clustering is to classification of similar entities. There are so many different techniques of data clustering available for different nature of applications. Data clustering techniques are categorizing into two types-Partitioning Procedures and Hierarchical Procedures. Hierarchical clustering creates hierarchy of clusters, look like tree. Results of hierarchical Clusters are shown in dendrogram shape. Partitioning method-clustering makes various partitions of objects and evaluates them by some standard. In this paper, we introduce a critical review on few papers and found some strengths and weaknesses of different clustering techniques. The purpose of this overview is to compare and evaluate each clustering techniques and find their pros and cons. This comparison concludes the better approach for future research in data clustering.