Research On The Covering And Its Application Based On Granular Computing

Granular computing (GrC) is viewed as an interdisciplinary study of computation in nature, society and science, characterized by structured thinking, structured problem solving and structured information processing with an underlying notion of multiple levels of granulation. It consists of all the theories, methodologies, techniques and tools related to the granularity, which is mainly used to deal with uncertainty, imprecise and incomplete information and seek resolutions from the large-scale massive dataset or complicated problem. Rough set, as a very important branch of GrC, is being improving and perfecting on theory and application as well as is being extending widely and significantly. Generalized rough set on covering is the one that partition's Pawlak rough set theory is extended into covering's. It focuses on the study of covering, so that many theories and applications in the Pawlak rough set are not tenable and suitable in the generalized rough set on covering. Therefore, this dissertation will mainly make research on covering theories and its applications under background of GrC, whose content is shown as follows:First of all, for the rules mining based on rough set theory in dynamic information system, a pre-process approach to eliminate the elements that cause inconsistence of rules mining in difference information system is proposed under the background of covering theory based on granular computing. Experiment shows that relationship between the changes of condition attributes values and trend of decision-making can be fully reflected as much as possible by a modified rules mining algorithm under the same time complexity through this pre-process approach.Secondly, for the conflict analysis, associated-conflict is firstly introduced in the perspective of GrC, and a reasonable and comprehensive approach to its analysis, using covering based on granular computing, is outlined. We argue that this model of associated-conflict analysis, given by the example of service-resource, will provide more profound insight for the conflict resolution in different fields.Thirdly, for the accuracy of classification method on single label dataset or multi label dataset, a unified paradigm for the accuracy used to evaluate different classification methods, using topological covering based on GrC, is presented, independent on number of data labels and different assumptions of ideal classification result(one assumption is partition, the other is covering). And some corresponding examples are also discussed to illustrate the accuracy in different classification situations. This unified paradigm will provide important reference value for the evaluation and improvement of accuracy of classification method.In brief, this paper discusses theories and applications related to the covering under the same theory background, and it can be treated as supplement and development of generalized rough set on covering. And it reflects the specificity on theories, methodologies, techniques and tools of knowledge discovery under the background of GrC, with significant referred and applied value in the future.