Fuzzy Granulation Method for Hybrid Big Data Based on K-plane Clustering
Conventional Hybrid big data fuzzy granulation often uses neighborhood mutual information entro-py algorithm.However,due to the lack of calculation of attribute importance,the simplification of data granula-tion is relatively low,and the granulation quality is not satisfactory.Thus,the paper presents a fuzzy granulation method for hybrid big data based on k-plane clustering.Based on the principle of fuzzy granulation of Hybrid big data sequences,time-series decomposition was used to decompose big data.The attributes with similar depend-encies were treated as information granules to calculate the importance of a single attribute,realizing the dimen-sionality reduction of big data.Combined with k-plane clustering algorithm,modal decomposition on the data was performed to achieve data partitioning.Thereby,the reducible granularity interval of the data was calculated,a-chieving fuzzy granulation of big data within the scope.The experimental results show that this method for fuzzy granulation of Hybrid big data can be conductive to data reduction and improve the granulation quality.
k-plane clusteringhybrid big datafuzzy granulationgranulation quality
NETL
NSTL
万方数据
维普
