Auto-assemblage for Suffix Tree Clustering

Due to explosive growth of extracting the information from large repository of data, to get effective results, clustering is used. Clustering makes the searching efficient for better search results. Clustering is the process of grouping of similar type content. Document Clustering; organize the documents of similar type contents into groups. Partitioned and Hierarchical clustering algorithms are mainly used for clustering the documents. In this paper, k-means describe the partitioned clustering algorithm and further hierarchical clustering defines the Agglomerative hierarchical clustering and Divisive hierarchical clustering. The paper presents the tool, which describe the algorithmic steps that are used in Suffix Tree Clustering (STC) algorithm for clustering the documents. STC is a search result clustering, which perform the clustering on the dataset. Dataset is the collection of the text documents. The paper focuses on the steps for document clustering by using the Suffix Tree Clustering Algorithm. The algorithm steps are display by the screen shots that is taken from the running tool. Keywords— Data Mining, Document Clustering, Hierarchical Clustering, Information Retrieval, Partitioned Clustering, Score Function, Similarity Measures, Suffix Tree Clustering, Suffix Tree Data model, Term Frequency and Inverse Document Frequency.