Soft Set Theory And Its Application In Knowledge Acquisition In

Today the databases’number and scale has been growing in high speed due to the rapid development of computer and network information technology. As a result, people are confronted with huge amounts of data containing abundant uncertainty. In knowledge discovering there are some mathematical theories and tools dealing with different forms of uncertainty information, such as probability theory, fuzzy set theory and rough set. However, it is hard to use only one of them to deal with all uncertainty problems especially in processing huge amounts of data, because these theories have their own limitations. Soft set theory has been proposed to solve uncertainty, which could provide a unified framework to deal with variety of uncertainty (including randomness, fuzziness and incompleteness and indistinguishable, etc.). As an extension of common fuzzy set and rough set, soft set theory provides good complementary for those limitations. Soft set theory describes uncertainty by means of domain and parameters’space so it can describe more abundant information and arithmetic operation than fuzzy set and rough set theory do. In uncertainty information theory, decision analysis, pattern recognition, data mining and other fields, soft set theory has potential superiority. Therefore soft set has important significance on theoretical research and practical application in dealing with uncertainty problems.Based on thorough investigation on soft set theory results, this paper gives a systematic analysis of the different meanings and methods between rough set and soft set in acquiring knowledge. With information and decision system as the research objects, the author has continued to explore soft set theory and its relevant applications by using the key technology of knowledge acquisition. The soft set theory has been enriched and developed. The main work of this paper is as follows:Firstly, two knowledge reductions are mainly discussed in knowledge acquisi-tion, containing attribute reduction which has kept classification ability unchanged and parameters reduction which has kept optimal decision object unchanged. Pa-rameters reduction is mainly used to solve the problem of reduction of soft decision, while attribute reduction is more widely used. This paper discusses the parameters reduction of soft set and gives much more effective reduction algorithm. The truth degree of attribution of soft sets’approximate functions is developed. Attributes are increased for reduction gradually in a top-down way with the truth degree of attribution as heuristic information until the reduction results are obtained. The proposed attribute reduction algorithm of soft sets can obtain the same result as rough set attributes reduction does. And also it has good reduction quality and high reduction efficiency, which is suitable for processing large data sets with redundant attributes.Secondly, we deal with association rule mining by expanding value soft set model, which is an important content in real practice. In order to make the organi-zation form of soft sets for table data more beneficial to data mining, the notion of inclusion degree、association rules and maximal association rules between two sets of the attributes are proposed. We point out an effective approach using inclusion degree of soft set for association rules mining. The validity of this method has been verified by the example comparative analysis. The algorithm’s complexity is de-creased. This method is more advantageous to mine meaningful value dependence rules and is easier for dealing with multiple parameters, mass data information tables.Finally, in acquisition technology of incomplete information system decision rules, optimal credible rules are obtained by expanding decision soft set model of incomplete information. By using descriptive formula of dominance decision making, we propose the concept of dominance decision-making descriptive language to obtain all determined decision rules in incomplete decision soft sets. Then soft simplified discernible matrix method is used to extract and reduce credible rule. Soft set method can obtain decision rules with more abundant information, which are short decision rules and have high supporting degree.