Review on Text Clustering Using Statistical and Semantic Data

The explosive growth of information stored in unstructured texts created a great demand for new and powerful tools to acquire useful information, such as text mining. Document clustering is one of its the powerful methods and by which document retrieval, organization and summarization can be achieved. Text documents are the unstructured databases that contain raw data collection. The clustering techniques are used group up the text documents according to its similarity. As there is a huge amount of unstructured data and there is a semantic correlation between features of data it is difficult to handle that. There are large no of feature selection methods that are used to used to improve the efficiency and accuracy of clustering process. The feature selection was done by eliminate the redundant and irrelevant items from the text document contents. Statistical methods were used in the text clustering and feature selection algorithm. The semantic clustering and feature selection method was proposed to improve the clustering and feature selection mechanism with semantic relations of the text documents.