Abstract
The size of collected data is increasing and the number of rules generated on those datasets is getting bigger. Producing compact and accurate models is being the most important task of data mining.
In this research work, we develop a new associative classifier – ACHC, that utilizes agglomerative hierarchical clustering as a post-processing step to reduce the number of rules and a new method is proposed in the rule-selection step to increase classification accuracy.
Experimental evaluations show that the ACHC method achieves significantly better results than classical rule learning algorithms in terms of rules on bigger datasets while maintaining classification accuracy on those datasets. More precisely, ACHC achieved the highest (43) result on the average number of rules and the third-highest (84.8%) result in terms of average classification accuracy among 10 classification algorithms.
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Acknowledgement
The authors gratefully acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the Horizon2020 Widespread-Teaming programme and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). They also acknowledge the Slovenian Research Agency ARRS for funding the project J2-2504. Jamolbek Mattiev is also funded for his Ph.D. by the “El-Yurt-Umidi” foundation under the Cabinet of Ministers of the Republic of Uzbekistan.
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Mattiev, J., Kavšek, B. (2021). ACHC: Associative Classifier Based on Hierarchical Clustering. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_55
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