Concept Partition Based on Cloud and Its Application to Mining Association Rules
Converting quantitative attributes into Boolean attributes is the general way for mining quantitative association rules. So how to reasonably partition domain values is very important. Traditional method can not get the easy to understand knowledge because it can not reflect the actual data distribution or the partition is too sharp. In this paper, a new method——cloud transform, which uses many concepts represented by cloud model to fit the real distribution of data——is introduced. This method can reflect the distribution of data in that domain while keeping the soft boundaries. Therefore, the discovered association rules are also easy to understand.
NETL
NSTL
万方数据
维普
