Association Rules Mining by Improving the Imperialism Competitive Algorithm (ARMICA)

Abstract : Many algorithms have been proposed for Association Rules Mining (ARM), like Apriori. However, such algorithms often have a downside for real word use: they rely on users to set two parameters manually, namely minimum Support and Confidence. In this paper, we propose Association Rules Mining by improving the Imperialism Competitive Algorithm (ARMICA), a novel ARM method, based on the heuristic Imperialism Competitive Algorithm (ICA), for finding frequent itemsets and extracting rules from datasets, whilst setting support automatically. Its structure allows for producing only the strongest and most frequent rules, in contrast to many ARM algorithms, thus alleviating the need to define minimum support and confidence. Experimental results indicate that ARMICA generates accurate rules faster than Apriori.
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Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.242-254, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_21〉
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S. Ghafari, Christos Tjortjis. Association Rules Mining by Improving the Imperialism Competitive Algorithm (ARMICA). Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.242-254, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_21〉. 〈hal-01557645〉

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