Distributed Concept Lattice Feature Matching Analysis in Large Data Set

To solve the problems of low accuracy and long time-consuming in feature matching of concept lattices, a distributed feature matching method of concept lattices in large data sets is proposed. By collecting the features of Distributed Concept Lattice and preprocessing the feature matching according to the results of feature acquisition, in order to ensure the accuracy of matching results, the classification criteria of features in large data sets are standardized. The feature matching of distributed concept lattices is completed according to the specification results. Experiments show that feature matching of Distributed Concept Lattices in large data sets is more accurate and time-consuming than traditional feature matching methods.