Résumé
Generally, the classical problem of gradual pattern mining involves generating pattern candidates and determining the number of concordant object pairs associated with them. Given a numeric data set with n objects and m features, each feature yields two gradual items. Gradual pattern candidates can be formed by combining different sets of gradual items. In fact, a gradual pattern is composed of gradual items with similar concordant object pairs. However, computing the object pairs for each item has a complexity that is approximately quadratic in terms of the number of objects. As the main contribution of this paper, we propose finding gradual patterns by clustering gradual items based on their similarity in object pairs. First, we project the object pairs of each gradual item onto an n-dimensional subspace, thus reducing the complexity of computing object pairs from a quadratic function to a linear function. Second, we group gradual items into r clusters based on the similarity of object pairs in the n-dimensional subspace. As part of our experiments, we evaluated our approach using a variety of clustering algorithms. We found that the best clustering algorithms (across all the data sets we used) achieved precision scores above 55%, recall scores close to 100%, and F1 scores above 71%.