Research On Granular Computing-based Approaches For Dynamic Knowledge Updating In Rough Set Theory

With rapid development of modern information technology, different types of data increase sharply. How to discovery knowledge timely and effectively from the big data is an extremely urgent problem. And the most important thing is the data in the different applications evolve dynamically, i.e., the new (old) data are inserted (deleted) continually, error data or values need to be revised in the database. How to update knowledge in time and effectively while data varies dynamically is an important research topic. It’s particularly essential to the applications of big data or interactive one. Dynamic knowledge discovery aims to improve the efficiency of the knowledge discovery and satisfies the needs in different applications. Granular computing provides a framework of divide and conquer, and multi-level for data processing, which is one of effective theories for processing big data. In this thesis, we focus on study several key problems of the dynamic knowledge discovery based on rough set theory by using the methodology of granular computing. The main research works and innovations are as follows:1. The dynamic properties of the measure of the knowledge granularity and the partitions are described firstly considering the cases of inserting an object or deleting an object. Then, the variation principle of knowledge granules in the case of attribute domain keeping unchanged (changed) are presented. Furthermore, the variation properties of the relative degree of mis-classification in the cases that knowledge granule increases (decreases) and concept granule keeps unchanged (increases) are investigated. Principles and algorithms for effectively up-dating approximations of variable precision rough set model while the universe varies are given. Experiments have been carried out in UCI data sets which verify the effectiveness of the algorithm.(Chapter3)2. A dominance-characteristic relation based rough set is proposed which may be used to pro-cess incomplete and ordering information. The definitions of up (down) multi-level (single-level) attribute coarsening or refining are given in incomplete order decision systems. The dynamic properties of dominating classes and dominated classes are investigated in different cases of attribute coarsening and refining. Then, the principles and algorithms for updating approximations are proposed under the dominance-characteristic relation based rough set model while the value domain varies. Experimental results verify the effectiveness of the algorithms and show that the performance of algorithms is related to the granularities of the dominating classes and dominated classes.(Chapter4) 3. A minimum discernibility attribute set is defined in classical rough set model. Then, the properties of knowledge granule and relevant parameters are described in complete decision systems under the coarsening or refining of attribute values. The dynamic properties of the measures of the rules are presented. In addition, the relationship between the general decision of the equivalence classes and approximations are investigated. A decision feature matrix in classical rough set model is defined. The dynamic properties of equivalence classes, gen-eral decision of the equivalence classes, the importance of attributes, and the discernibility attributes with regard to the coarsening or refining of attribute values are investigated. Fur-thermore, propositions and algorithms for updating rules via updating the decision feature matrix, the assignment discernibility attribute matrix and the minimal discernibility attribute set are given. The effectiveness of the algorithm is verified by experiments.(Chapter5)4. An equivalence feature matrix and a characteristic value matrix in the information system are defined firstly. The variations of the granularities under decision-theoretic rough set mod-el are analyzed when objects and attributes are added simultaneously, respectively. Then, the information system is decomposed into three subspaces considering the different effect-s to the granularities by objects and attributes. The relationship between granules and the effect to the equivalence feature matrix as well as the properties of dynamically updating approximations are discussed in the different subspaces. Furthermore, algorithms based on the granule are developed to update approximation of decision theoretic rough set model dy-namically while attributes and objects evolve with time simultaneously. Experimental results verify the effectiveness of the algorithms.(Chapter6)The works in the thesis extend the research field of the rough set theory and it’s appli-cations, and present the principles and algorithms based on granular computing for dynam-ic knowledge discovery, which improve the efficiency of knowledge discovery. The research achievements have certain theoretical and practical significance to the analysis of big data.