Abstract
In this paper a newclassification algorithm based upon frequent patterns is proposed. A frequent pattern is a generalization of the concept of a frequent item set, used in association rule mining. First of all, the collection of frequent patterns in the training set is constructed. For each frequent pattern, the support and the confidence is determined and registered. Choosing an appropriate data structure allows us to keep the full collection of frequent patterns in memory. The proposed classification method makes direct use of this collection. This method turns out to be competitive with a well-known classifier like C4.5 and other comparable methods. For large data sets it seems to be a very appropriate method.
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Pijls, W., Potharst, R. (2001). Classification Based upon Frequent Patterns. In: Kowalczyk, R., Loke, S.W., Reed, N.E., Williams, G.J. (eds) Advances in Artificial Intelligence. PRICAI 2000 Workshop Reader. PRICAI 2000. Lecture Notes in Computer Science(), vol 2112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45408-X_8
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DOI: https://doi.org/10.1007/3-540-45408-X_8
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