Stock-selecting Based On Rough Sets And Searching Optimal Buying-selling-price Based On Random Walk

The rough set theory, introduced by Pawlak in 1982[1,2], is a useful tool for the analysis of inexact, uncertain, or vague data. The technique called rough analysis can be applied very fruitfully in artificial intelligence and cognitive sciences. The method of rough set theory is suitable for the problems of attribute analysis and knowledge in database (KDD). In rough sets a table called an information system is used to represent knowledge. An information system is a generalization of data relation. Knowledge is defined as classification of information systems. In one part of this paper, rough sets theory (including attribute dependence, reduction of attribute table, minimum decision algorithm generation ect.) are summarized and proposed. We try to apply the rough analysis technique to stock database. First, we change the stock data to information systems by the use of stock theory, then reduce the stock attribute table to the minimum decision algorithm generation, and finally apply it to chose the stock which could be invested .In another part of this paper, we construct a model to search the optimal buying-selling price based on random w~lk theory.