Incremental approximation computation in incomplete ordered decision systems

Abstract

Approximation computation is a critical step in rough sets theory used in knowledge discovery and other related tasks. In practical applications, an information system often evolves over time by the variation of attributes or objects. Effectively computing approximations is vital in data mining. Dominance-based rough set approach can handle information with preference-ordered attribute domain, but it is not able to handle the situation of data missing. Confidential Dominance-based Rough Set Approach (CDRSA) is introduced to process Incomplete Ordered Decision System (IODS). This paper focuses on incremental updating approximations under dynamic environment in IODS. With the CDRSA, the principles of incremental updating approximations are discussed while the variation of attribute sets or the union of subsets of objects and the corresponding incremental algorithms are developed. Comparative experiments on data sets of UCI and results show that the proposed incremental approaches can improve the performance of updating approximations effectively by a significant shortening of the computational time.