An Estimation of Distribution Algorithms Applied to Sequence Pattern Mining
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Abstract
This paper presents a proposal of distribution’s estimated algorithm to the extraction of sequential patterns in a database which use a probabilistic model based on graphs which represent the relations among items that form a sequence. The model maps a probability among the items allowing them to adjust the model during the execution of the algorithm using the evolution process of EDA, optimizing the candidate’s generation of solution and extracting a group of sequential patterns optimized.
Keywords
Probabilistic Model Sequential Pattern Minimum Support Pattern Mining Frequent Sequence
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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