Mining Knowledge Of The Directed Acyclic Graph (DAG) And Dataset Using The Hill Climbing Algorithm
[Full Text]
AUTHOR(S)
Munirah, Aslan Alwi
KEYWORDS
Hill Climbing algorithm, Set of rules, Mining Knowledge, Datasets, Optimal DAG, IFThen, Bayes Network.
ABSTRACT
Hill Climbing Algorithm is used by people to produce bayes (bayes network) symptoms in the form of directed acyclic grpah (DAG). With this algorithm look for the optimal DAG of a dataset. However, a DAG is a symptom of causality / causation of bayes so that the optimal DAG search of a dataset is equivalent to the search for symptom causality that is most likely (optimum) between attributes or data variables. This means finding knowledge in the form of a causal relationship. Therefore, it is reasonable to mine the form of knowledge expressed in the form of rules from DAG by converting trending arrows between nodes as ifthen relationships between variables. In this study, it was proposed how to mine knowledge (set of rules) from the dataset by using the optimal DAG from a dataset assuming that the optimal DAG produces the most optimal set of rules. Rule mining in this way uses hill climbing algorithms as a tool to produce optimal DAG. There are algorithms other than hill climbing such as ACO or Genetic algorithms, but the choice is dropped on hill climbing algorithms as the first trial of research.
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