Association feature mining algorithm of web accessing data in big data environment

Abstract

The current method of data mining has the problem of low accuracy. Therefore, this paper proposes a fuzzy clustering algorithm based on chaotic and dynamic variation shuffled frog leaping algorithm (SFLA). Firstly, the data association feature is denoised, and then the correlation feature is extracted by combining the denoising data. By using the fuzzy clustering algorithm based on chaotic and dynamic variation SFLA, the data are clustered to complete association feature mining of Web accessing data. Experimental results show that the algorithm can effectively improve the accuracy of data mining.

Keywords:

• Big data environment
• Association feature
• Data denoising
• Shuffled frog leaping algorithm