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. However, it represents a challenge when dealing with a big number of data due to high dimensionality of the feature space and to the semantic correlation between features. In this paper, we propose a new sequential document clustering algorithm that uses a statistical and semantic feature selection methods. The semantic process was proposed to improve the frequency mechanism with the semantic relations of the text documents. The proposed algorithm selects iteratively relevant features and performs clustering until convergence. To evaluate its performance, experiments on two corpora have been conducted. The obtained results show that the performance of our algorithm is superior to that obtained by the existing algorithms.