Sentence examples for discovering frequent patterns from inspiring English sources

In this paper, we propose a fast, load balancing and resource efficient algorithm named FLR-Mining for discovering frequent patterns in distributed computing systems.

Although the past studies can effectively discover frequent patterns to deduce association rules, execution efficiency is still a critical problem.

To discover frequent patterns more efficiently, various studies such as applying cellular learning automata [13] have been conducted.

To discover topics, we converted each sentence of the document into a transaction format, and employed association rule mining algorithms to discover frequent patterns from the documents.

Since this group of cells can be large in the dataset, they can provide (on average) meaningful information to discover frequent patterns.

From these networks we discovered frequent patterns that occur in all networks and at different size levels.

In the method section, probability isomorphic algorithm is described in details and the two-step hierarchical clustering method for discovering frequent probability pattern is introduced.

FPT method discovers frequent patterns in transactional databases by FP-growth arithmetic.

Then based on the probability graph isomorphism algorithm, the hierarchical clustering method is adopted to cluster subgraphs to discover frequent probability patterns from the clusters.

To verify that discovered frequent probability patterns include reported motifs, we did not set frequence degree in the process of clustering.

Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters.