Theory and method of granular computing for big data mining
Liang‐Wen Ji · Scientia Sinica Informationis · 2015
The external form of big data often presents large-scale, multiple modal, and growth characteristics. In this paper, we discuss and analyze the challenges in data mining from the viewpoint of big data; these challenges include computability, effectiveness, and efficiency. Granular computing is an effective method for solving complex problems for intelligent information processing. By analyzing the feasibility of large data analysis based on granular computing, we argue that granular computing shows great promise as a new way for data mining in the context of big data. We also analyze several important problems in data mining based on granular computing, and the results will lead to further interpretations and developments in the field of big data mining.