A multiview approach for intelligent data analysis based on data operators

Multiview intelligent data analysis explores data from different perspectives to reveal various types of structures and knowledge embedded in the data. Each view may capture a specific aspect of the data and hence satisfy the needs of a particular group of users. Collectively, multiple views provide a comprehensive description and understanding of the data. In this paper, we propose a multiview framework of intelligent data analysis based on modal-style data operators. The classes of the data operators include basic set assignment, sufficiency, dual sufficiency, necessity and possibility operators. They demonstrate various types of data relationships and characterize various features and granulated views of the data. It is shown that different structures of the data can also be constructed based on the different data operators.