By extracting frequent patterns efficiently, it is possible to enhance some existing algorithms. Using many candidate patterns causes the results of the classification model to be more powerful. Moreover, aggregating similar items within patterns increases the possibility of creating more powerful patterns. In our method, we define some taxonomies and extract more powerful frequent patterns to incorporate such taxonomies and items. Our aim is to improve Classification by Aggregating Emerging Patterns(CAEP) by using more promising patterns with taxonomy. Using certain computational experiments as a source of practical data, we show that our performance is better than the one that does not use taxonomy. By identifying the reason behind our performance, we show that our method can extract better candidate patterns incorporating taxonomy.
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