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Frequent pattern mining is an essential theme in data mining.
It was a stark reversal of the frequent pattern nationally of judges' reducing large jury awards.
Parallel frequent pattern discovery algorithms exploit parallel and distributed computing resources to relieve the sequential bottlenecks of current frequent pattern mining (FPM) algorithms.
We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events.
The PrePost algorithm is one of well-known algorithms of frequent pattern mining.
Frequent itemset (or frequent pattern) mining is a very important issue within the data mining field.
The fuzzy frequent pattern (FFP -tree and the compressed FFP -treequent pandern (CFFP)-thee algorithms were respecompressedoposed to mine the incomplete fuzzy frequent itemsets from the tree-based structures.
The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues.
So in the paper we aim to use the technique of frequent pattern mining to find out these events.
While many frequent pattern mining algorithms handle precise data, there are situations in which data are uncertain.
Experimental results show the benefits of our tightened upper bounds to expected supports in uncertain frequent pattern mining.
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