Clustering is the process of grouping or aggregating of data items. Sentence clustering mainly used in variety of applications such as classify and categorization of documents, automatic summary generation, organizing the documents, etc. In text processing, sentence clustering plays a vital role this is used in text mining activities. Size of the clusters may change from one cluster to another. The traditional clustering algorithms have some problems in clustering the input dataset. The problems such as, instability of clusters, complexity and sensitivity. There are several algorithms available for clustering. Each algorithm will cluster or group similar data objects in a useful way. This task involves dividing the data into various groups called clusters. The application of clustering includes Bioinformatics, Business modeling, image processing etc. In general, the text mining process focuses on the statistical study of terms or phrases which helps us to understand the significance of a word within a document. Even if the two words didn’t have similar meanings, clustering will takes place. Clustering can be considered the most important unsupervised learning framework, a cluster is declared as a group of data items, which are “similar” between them and are “dissimilar” to the objects belonging to other clusters. Sentence Clustering mainly used in variety of text mining applications. Output of clustering should be related to the query, which is specified by the user.