Examinando por Autor "Blanco, Ignacio J."
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Ítem Incremental maintenance of discovered association rules and approximate dependencies(IOS Press, 2017-01) Pérez, Alain; Blanco, Ignacio J.; González-González, Luisa M.; Serrano, José M.Association Rules (ARs) and Approximate Dependencies (ADs) are significant fields in data mining and the focus of many research efforts. This knowledge, extracted by traditional mining algorithms becomes inexact when new data operations are executed, a common problem in real-world applications. Incremental mining methods arise to avoid re-runs of those algorithms from scratch by re-using information that is systematically maintained. These methods are useful to extract knowledge in dynamic environments. However, the implementation of algorithms only to maintain previously discovered information creates inefficiencies. In this paper, two active algorithms are proposed for incremental maintenance of previous discovered ARs and ADs, inspired by efficient computation of changes. These algorithms operate over a generic form of measures to efficiently maintain a wide range of rule metrics simultaneously. We also propose to compute data operations at real-time, in order to create a reduced relevant instance set. The algorithms presented do not discover new knowledge; they are just created to efficiently maintain previously extracted valuable information. Experimental results in real education data and repository datasets show that our methods achieve a good performance. In fact, they can significantly improve traditional mining, incremental mining, and a naïve approach.Ítem Incrementalmaintenance of discovered fuzzy association rules(Springer, 2021-03-31) Pérez-Alonso, Alain; Blanco, Ignacio J.; Serrano, José M.; González-González, Luisa M.Fuzzy association rules (FARs) are a recognized model to study existing relations among data, commonly stored in data repositories. In real-world applications, transactions are continuously processed with upcoming new data, rendering the discovered rules information inexact or obsolete in a short time. Incremental mining methods arise to avoid re-runs of those algorithms from scratch by re-using information that is systematically maintained. These methods are useful for extracting knowledge in dynamic environments.However, executing the algorithms only to maintain previously discovered information creates inefficiencies in real-time decision support systems. In this paper, two active algorithms are proposed for incremental maintenance of previously discovered FARs, inspired by efficient methods for change computation. The application of a generic form of measures in these algorithms allows the maintenance of a wide number of metrics simultaneously. We also propose to compute data operations in real-time, in order to create a reduced relevant instance set. The algorithms presented do not discover new knowledge; they are just created to efficiently maintain valuable information previously extracted, ready for decision making. Experimental results on education data and repository data sets showthat our methods achieve a good performance. In fact, they can significantly improve traditional mining, incremental mining, and a naïve approach.