This paper presents methods of dividing quantitative attributes into fuzzy domains with multi-granularity representation of data based on hedge algebra approach. According to this approach, more information is expressed from general to specific knowledge by explored association rules. As a result, this method brings a better response than the one using usual single-granularity representation of data. Furthermore, it meets the demand of the authors as the number of exploring rules is higher. | Journal of Computer Science and Cybernetics, , (2018), 63–75 DOI PARTITION FUZZY DOMAIN WITH MULTI-GRANULARITY REPRESENTATION OF DATA BASED ON HEDGE ALGEBRA APPROACH TRAN THAI SON1 , NGUYEN TUAN ANH2 1 Institute of Information Technology, Vietnam Academy of Science and Technology 2 University of Information and Communication Technology, Thai Nguyen University 1 ttson1955@ Abstract. This paper presents methods of dividing quantitative attributes into fuzzy domains with multi-granularity representation of data based on hedge algebra approach. According to this approach, more information is expressed from general to specific knowledge by explored association rules. As a result, this method brings a better response than the one using usual single-granularity representation of data. Furthermore, it meets the demand of the authors as the number of exploring rules is higher. Keywords. Fuzzy association rule, algebra approach, multi-granularity, Data mining, membership functions 1. INTRODUCTION In terms of exploring knowledge in the studies, the problem of determining of fuzzy domain of data is quantitative attributes are more and more significantly attracted. This is a considerably initial step for the whole process of information processing for most of later data mining problems, such as association rule mining, classification, identification, regression [2, 4, 3, 10, 14]. If we have a reasonable fuzzy partition, the knowledge discovered will better reflect the hidden rules in the information store. Vice versa, if there is no proper fuzzy partition at first, the knowledge which we explore may be subjective, imposing and not exactly. This is not a simple problem. First, it primarily relates to the perception of the individual and depends on the context. For example, in the attribute domain “distance”, it is not easy to determine when it is called “far” or “relatively close”. Moreover, fuzzy division much depends on
