Attribute Reduction Based on Attribute Similarity and with Application to Logging Interpretation

Due to the explosive growth of electronically stored information, automatic methods must be developed to maintain and use this abundance of information effectively. In particular, the sheer volume of redundancy present must be dealt with, leaving only the information-rich data to be processed. This paper presents a novel approach of attribute reduction based on attribute similarity, to greatly reduce this data redundancy. The work is applied to the quantitative computation of reservoir parameter, considerably reducing dimensionality with minimal loss of information. Experimental results show that the method used in this paper is not only more powerful but also finer than the conventional rough set-based approach, and it has high fitting precision and quick rate of convergence.