Granular Computing Based Knowledge Discovery And Its Applications

Knowledge discovey is a hot research field in artificial intelligent. However, there are still many un-resolved problems in this field, such as the meagar of the knowledge presentation, knowledge extraction under complex data and data environments (for example, the incremental data environment), reduct the features of high dimensional data and evalution for the knowledge after knowledge discovery etc.This dissertation addresses on several steps, which are knowledge representation/description, knowledge extraction, reduct dimension of the knowledge raw data and the evualtion for the knowledge. It introduces theory of granular computing and improves the drawbacks containing the previous steps. Including the following detail:(1) We conclude three basic principles in granular computing; they are granular knowledge representation, granular approximate problem resovling, and granular problem mapping.(2) The classification under different granula is treated as the knowledge representation. The different scale of the granula can be reflected by the classification of raw data, which is also knowledge in real world.(3) We present the knowledge extracting algorithm which supporting inconsistent data The algorithm is based on the rough set theory in granular computing, and it can also be improved as an algorithm for incremental data set. After applying the principle of granular approximate problem resovling, this algorithm can also be expanded to a parallel/serialized approximate knowledge extracting algorithm.(4) We present a hybrid feature ruduct and feature selection algorithm. It defines a measure named data inconsistent ratio from the intuition of the specific table containing the stable number of equivalent classes.(5) We also use the practice problem to evulate the knowledge discovery methods. A real problem for distinguishing the domain knowledge of the ancient Chinese architecture modeling system has been proposed...