关键词: 大数据, 粗糙集, 属性约简, 并行计算, 增量学习, 粒计算

Abstract: Classical analysis tools are not able to satisfy this era of big data, which is full of multifarious, complicated and dynamic changed data. How to obtain valuable information from large-scale data quickly and effectively has became a challenging problem. Some scholars combined the rough set attribute reduction theory with other theories to process high-dimensional, dynamic and massive data effectively. The attribute reduction algorithms based on parallel computing, incremental learning and granular computing are classified and summarized. Then their characteristics, present problems and the key future research directions are analyzed.

Key words: big data, rough set, attribute reduction, parallel computing, incremental learning, granular computing