Efficient Redundancy Processing Framework of Association Rules Model based on Hypergraph in Information Pattern Recognition

摘要

Efficient redundancy processing framework of the association rules model based on hypergraph in the information pattern recognition is designed in this paper. For big data analysis or pattern recognition problems, data sets have the characteristics of the diverse formats and sparse information, it is essential to extract the vital information to obtain the efficient analysis of the data sets. The interval value attribute data set is a data set containing multiple attribute data. These data are clustered together, and the types are huge, and it is difficult to reach agreement when classifying. Hence, his feature is used in the research work to construct the novel analytic framework. The experiment results have proven that the proposed algorithm can effectively process the data with higher accuracy.