Fuzzy association rules is introduced in 3]. However, the algorithms proposed by 3] for mining fuzzy association rules assumes that fuzzy sets are given. Here we propose a method to nd the fuzzy sets based on clustering techniques. We have implemented our proposed method and showed that it is feasible and produces desirable results.
An attribute-oriented induction has been developed in the previous study of knowledge discovery in databases. A concept tree as-cension technique is applied in concept generalization. In this paper, we extend the background knowledge representation from an unconditional non-rule-based concept hierarchy to a rule-based concept hierarchy , which enhances… (More)
Increasing number of organizations have computing clusters located in different places. The distance between the computing clusters can be quite far apart. The load in one cluster may be very high while the other clusters may have nothing running on the system. A higher throughput can be achieved if load balancing is added between the clusters. In this… (More)
This work investigates the performance of parallel R-tree where the spatial data are high dimensional objects. Due to the large number of bounding values for each bounding box in the R-tree, one disk page might contain one bounding box only. Parallel Binary R-tree(PBR-tree) is proposed to facilitate the costly node access. An analytical performance… (More)