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Hierarchical Classification is a very important classification task for arranging data in a hierarchical structure. Hierarchical arrangement of data is one of the best methods to achieve better understanding of complex data. In this paper, we propose the HMAC method to perform Hierarchical Multi-label Associative Classification. This method uses multiple and negative rules to predict class-set and...
Associative classification presents various methods whose common characteristic is the class prediction from the class association rules (rules whose consequent one is one of the class modalities). According to and, this new approach offers better results than the traditional approaches based on rules such as the decision trees. It also offers a great flexibility with the unstructured data. However,...
Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules is one of the most effective classification method, whose accuracy is higher and discovered rules are easier to understand comparing with classical classification methods. However, current algorithms for classification...
This research compares the performance of three popular association rule mining algorithms, namely apriori, predictive apriori and tertius based on data characteristics. The accuracy measure is used as the performance measure for ranking the algorithms. A wide variety of association rule mining algorithms can create a time consuming problem for choosing the most suitable one for performing the rule...
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