The goal of this survey is to provide a comprehensive review of different clustering techniques in data mining. Data mining refers to extracting useful information from vast amounts of data. It is the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. An important technique in data analysis and data mining applications is Clustering.Cluster Analysis is an excellent data mining tool for a large and multivariate database. Clustering is a suitable example of unsupervised classification. Unsupervised means that clustering does not depend on predefined classes and training examples during classifying the data objects. Each group called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. There are different types of clustering algorithms such as hierarchical, partitioning, grid, density based, model based, and constraint based algorithms. Hierarchical clustering is the connectivity based clustering. Partitioning is the centred based clustering; the value of k-mean is set. Density based clusters are defined as area of higher density then the remaining of the data set. Grid based clustering is the fastest processing time that typically depends on the size of the grid instead of the data. Model based clustering hypothesizes for each cluster and find the best fit of data to the given model. Constraint based clustering is performed by incorporation of user or application oriented constraints.In this survey paper, a review of different types of clustering techniques in data mining is done.