Weighted Label propagation algorithm with probability Threshold (p-wlpa) with typical serial execution prototype is proposed to be applied in data classification. Meanwhile on distributed computing system Spark, paral...
详细信息
ISBN:
(纸本)9781479944194
Weighted Label propagation algorithm with probability Threshold (p-wlpa) with typical serial execution prototype is proposed to be applied in data classification. Meanwhile on distributed computing system Spark, parallel p-wlpa algorithm for labeling big data is conducted. The algorithm sets prior conditions when configuring undirected graph and optimizes parameter learning in p-wlpaprocess. Through experiments, we analyze the relationship between Iterations for convergence, sample stability threshold and error rate. Finally Serial and parallel p-wlpaperformance comparision demonstrates the feasibility and efficiency of the parallel p-wlpa algorithm implementation on Spark.
Weighted Label propagation algorithm with probability Threshold(p-wlpa) with typical serial execution prototype is proposed to be applied in data classification. Meanwhile on distributed computing system Spark, parall...
详细信息
ISBN:
(纸本)9781479944187
Weighted Label propagation algorithm with probability Threshold(p-wlpa) with typical serial execution prototype is proposed to be applied in data classification. Meanwhile on distributed computing system Spark, parallel pwlpaalgorithm for labeling big data is conducted. The algorithm sets prior conditions when configuring undirected graph and optimizes parameter learning in p-wlpaprocess. Through experiments,we analyze the relationship between Iterations for convergence,sample stability threshold and error *** Serial and parallel p-wlpaperformance comparision demonstrates the feasibility and efficiency of the parallel p-wlpa algorithm implementation on Spark.
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