Distributed Mining Core of Attributes on Horizontally Partitioned Data
Dan Hu, Xianchuan Yu, Yuanfu Feng · 2008
The field of distributed data mining (DDM) has emerged as an active area in recent years because the key challenge in knowledge discovery is the extraction of knowledge from massive databases. Rough set theory (RST) is one of the powerful approaches in data mining, which has been demonstrated to have its usefulness in successfully solving a variety of problems. But there is almost no literature related to the distributed computation in RST. In this paper, the relation between the cores of partitioned data and global data are discussed. An useful proposition is obtained, which shows that the union of the cores of partitioned data is determinedly included in the core of global data. In following, two algorithms, DMC and PPDMC, are proposed for distributed mining of core on horizontally partitioned data. DMC concerns the reduction of time complexity while PPDMC focuses on privacy preserving. Experiment and propositions show the excellent function of DMC and PPDMC through practical and academic ways. Just because the pivotal status of core in RST, the algorithms proposed in this paper will show good foreground in distributed data mining.