The Research Of Structural Learning Method Based On Quotient Space

As an emerging research sub-field of artificial intelligence, granular computing, whose philosophy is to implement the problem solving at different levels of granularity, aims to establish much more general model reflecting the process of human problem solving. Granule, a clump of points (objects) drawn together by indistinguishability, similarity, proximity or functionality, is the primitive notion of granular computing.Quotient space theory of problem solving is generalized from two directions, namely the structure of the universe of discourse and granulation criteria. In the light of granular computing quotient space theory of problem solving exploits topology to describe the structure of the universe of discourse, and utilizes equivalence relation to implement granulation, and relies on the natural mapping to realize the translations among different levels of granulariy.As a method of problem solving, quotient space theory, based on substantial theory, considering the problem from different aspects and multi-hierarchy in the process of problem solving, is a kind of powerful tool in that it can decrease the difficulty of the problem and reduce the computational cost. Unifying the quantitative analysis and the qualitative analysis by utilizing quotient space theory, complex problems are represented by different granules based quotient space. After learning rules of different granules achieved, integrate rules of the complex problem can be gained by composing relative rules.The traditional clustering algorithms based on distance or similarity are vector-based, these methods are unfit for individual behavioral data, there will be a lot of information lost and will lead to the clustering inaccurate.Directed by the theory of quotient space, a improved algorithm of covering algorithm is presented. It can keep the sorting accuracy and cut down the occupying of memory and the cost of data collecting. The simulation experiment about dual spiral data shows the feasibility of the improved alternative coving algorithm proposed above.As a means of the rising artificial intelligence, quotient space theory and covering algorithm itself need more development and consummate.