Abstract
Huge amounts of data are being collected and analyzed nowadays. By using the popular rule-learning algorithms, the number of rules discovered on those big datasets can easily exceed thousands of rules. To produce compact and accurate classifiers, such rules have to be grouped and pruned, so that only a reasonable number of them are presented to the end user for inspection and further analysis. To solve this problem researchers have proposed several associative classification approaches that combine two important data mining techniques, namely, classification and association rule mining.
In this paper, we propose a new method that is able to reduce the number of class association rules produced by classical class association rule classifiers, while maintaining an accurate classification model that is comparable to the ones generated by state-of-the-art classification algorithms. More precisely, we propose a new associative classifier – CMAC, that uses agglomerative hierarchical clustering as a post-processing step to reduce the number of its rules.
Experimental results performed on selected datasets from the UCI ML repository show that CMAC is able to learn classifiers containing significantly less rules than state-of-the-art rule learning algorithms on datasets with larger number of examples. On the other hand, classification accuracy of the CMAC classifier is not significantly different from state-of-the-art rule-learners on most of the datasets.
We can thus conclude that CMAC is able to learn compact (and meaningful) classifiers from “bigger” datasets, retaining an accuracy comparable to state-of-the-art rule learning algorithms.
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Change history
30 March 2021
The original version of chapter 2 was inadvertently published with wrong RTS values in Table 3: “Results comparison with RTS, S, and SVMlight with standard linear loss with a 10-fold cross validation procedure.”
The RTS values were corrected by replacing the wrong values with the appropriate ones.
The footnote reads “1Code is available at: https://osf.io/fbzsc/” and has been added to the last sentence in the abstract.
The original version of chapter 34 was inadvertently published with incorrect allocations between authors and affiliations resp. one affiliation was entirely missing.
The affiliations have been corrected and read as follows: 1University of Primorska, Koper, Slovenia; 2Jožef Stefan Institute, Ljubljana, Slovenia; and 3Urgench State University, Urgench, Uzbekistan. The authors' affiliations are: Jamolbek Mattiev1,3 and Branko Kavšek1,2.
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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 program and the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Union of the European Regional Development Fund). 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. (2020). CMAC: Clustering Class Association Rules to Form a Compact and Meaningful Associative Classifier. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_34
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