A Survey on Data Clustering Algorithms based on Fuzzy Techniques

Clustering is used to describe methods for grouping of unlabeled data. Clustering is an important task in data mining to group data into meaningful subsets to retrieve information from a given dataset. Clustering is also known as unsupervised learning since the data objects are pointed to a collection of clusters which can be interpreted as classes additionally. The chief objective of the clustering is to present a collection of similar records. The clustering problem has been focused by many researchers. Data Clustering is a technique in which logically similar information is physically stored together. The numbers of disk accesses are to be minimized in order to increase the efficiency in the database systems. The basic data clustering problem might be defined as finding out groups in data or grouping related objects together. Many different clustering techniques have been proposed over the years such as Partitioning methods, Hierarchical methods, Density-based methods, Grid-based methods and Model-based methods. This paper deals with an attempt at studying the data clustering algorithms based on fuzzy techniques. These fuzzy clustering algorithms have been widely studied and applied in a variety of substantive areas.