关键词: 云计算, 粗糙集, 知识约简, 数据并行, 任务并行

Abstract: Knowledge reduction in rough set theory is the critical process of knowledge acquisition among data mining applications. Classical knowledge reduction algorithms assume all the datasets can be loaded into the main memory, while the existing parallel knowledge reduction algorithms only implement reduction tasks concurrently, which are infeasible for large-scale datasets. Massive data with high dimension makes attribute reduction a challenging task. To solve this problem, the concept of indiscernibility object pairs is defined and a new knowledge reduction algorithm for boundary region partition preserving is proposed. The relationship among these algorithms is illustrated in detail. Then, the parallelism strategies of data and task parallel are implemented and discussed. The corresponding attribute reduction framework model for boundary region partition preserving is presented. The experimental results demonstrate that knowledge reduction algorithms in cloud computing can efficiently process massive datasets on Hadoop platform.

Key words: cloud computing, rough set, knowledge reduction, data parallel, task parallel