High-Order Co-clustering Text Data on Semantics-Based Representation Model

The language modeling approach is widely used to improve the performance of text mining in recent years because of its solid theoretical foundation and empirical effectiveness. In essence, this approach centers on the issue of estimating an accurate model by choosing appropriate language models as well as smooth techniques. Semantic smoothing, which incorporates semantic and contextual information into the language models, is effective and potentially significant to improve the performance of text mining. In this paper, we proposed a high-order structure to represent text data by incorporating background knowledge, Wikipedia. The proposed structure consists of three types of objects, term, document and concept. Moreover, we firstly combined the high-order co-clustering algorithm with the proposed model to simultaneously cluster documents, terms and concepts. Experimental results on benchmark data sets (20Newsgroups and Reuters-21578) have shown that our proposed high-order co-clustering on high-order structure outperforms the general co-clustering algorithm on bipartite text data, such as document-term, document-concept and document-(term+concept).