Data Mining Method Research Based On Rough Set Theory In Incomplete Decision-making System

Rough set theory, introduced by Pawlak Z. in the early 1980s, is a new mathematical tool used for dealing with vagueness and uncertaint information to discover implicit knowledge or reveal latent laws. Classic rough set theory based on equivalence relation takes complete system as object of study , and divides the region into some non-intersect equivalence class; But, in the real life, because of the errors in data measuring, understanding of data, or the restriction in data collection , it can make the decision-making system incomplete, that is, value of attribution of some objects is unknown, which restrains development of the theory to practical direction. So how to acquire knowledge from incomplete decision-making system has been a crucial research topic recently.The paper, firstly, sums up the principles and reality of data mining, and discusses the corresponding concepts, working steps and key technologies about data mining from the viewpoint of data mining and knowledge classification, making a deep analysis about basic theories and extension in the incomplete system. Then, based on compatible relation, study of combining information theory with set theory and study of combining rough set theory with genetic algorithm for reduction have been made. Algorithms for reduction are validated by experiments. It shows that these algorithms can find corresponding reduction results. Next it puts forward an algorithm that can acquire directly decision-making rules in decision-making table. All algorithms are validated by experiments, the results of which show that these algorithms turn out right.At last, it establishes a system for data mining in incomplete decision-making system, applying the algorithms put forward in the paper, for attribution reduction and optimum rule extraction to fulfill the functions of the model.