Distributed Parallel Construction Algorithm for Triadic Concepts
As an extension of formal concept analysis,triadic concept analysis achieves significant results in both theory and applications of high-dimensional data.However,the time complexity of triadic concept generation algorithms,caused by the rapid growth of data volume,typically grows exponentially,presenting significant challenges in practical applications.Therefore,parallel algorithms are crucial.In this paper,a distributed parallel construction algorithm for triadic concepts suitable for large-scale data is proposed.First,the theories of object-attribute triadic concepts and attribute-condition triadic concepts are provided,and it is proved that all triadic concepts can be generated by merging these two types of intermediate concepts.Second,a two-stage aggregation strategy is employed to improve the resilient distributed dataset operator in the Spark framework.Consequently,the data skew problem is effectively solved and the efficiency of the proposed algorithm is significantly improved.Finally,experiments on multiple public datasets indicate that the proposed algorithm performs efficiently in generating triadic concepts for large datasets.
万方数据
维普
