A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation Export

@article{citeulike:2021211, abstract = {Associative-classification is a promising classification method based on association-rule mining. Significant amount of work has already been dedicated to the process of building a classifier based on association rules. However, relatively small amount of research has been performed in association-rule mining from multi-label data. In such data each example can belong, and thus should be classified, to more than one class. This paper aims at the most demanding, with respect to computational cost, part in associative-classification, which is efficient generation of association rules. This task can be achieved using different frequent pattern mining methods. In this paper, we propose a new method that is based on the state-of-the-art tree-projection-based frequent pattern mining algorithm. This algorithm is modified to improve its efficiency and extended to accommodate the multi-label recurrent-item associative-classification rule generation. The proposed algorithm is tested and compared with A priori-based associative-classification rule generator on two large datasets.}, author = {Rak, Rafal and Kurgan, Lukasz and Reformat, Marek}, booktitle = {Fourth International Conference on Business Process Management (BPM 2006) - Four selected and extended papers; 8th International Conference on Enterprise Information Systems (ICEIS' 2006) - Three selected and extended papers}, citeulike-article-id = {2021211}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1321784.1321948}, citeulike-linkout-1 = {http://dx.doi.org/10.1016/j.datak.2007.05.006}, citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6TYX-4P37JB9-1/2/4b6cb05539709fe49b63a77a755da50c}, doi = {10.1016/j.datak.2007.05.006}, journal = {Data \& Knowledge Engineering}, keywords = {classification, freq\_sets}, month = {January}, number = {1}, pages = {171--197}, posted-at = {2007-11-30 02:15:50}, priority = {4}, title = {A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation}, url = {http://dx.doi.org/10.1016/j.datak.2007.05.006}, volume = {64}, year = {2008} }

TY - JOUR ID - citeulike:2021211 L3 - citeulike-article-id:2021211 N2 - Associative-classification is a promising classification method based on association-rule mining. Significant amount of work has already been dedicated to the process of building a classifier based on association rules. However, relatively small amount of research has been performed in association-rule mining from multi-label data. In such data each example can belong, and thus should be classified, to more than one class. This paper aims at the most demanding, with respect to computational cost, part in associative-classification, which is efficient generation of association rules. This task can be achieved using different frequent pattern mining methods. In this paper, we propose a new method that is based on the state-of-the-art tree-projection-based frequent pattern mining algorithm. This algorithm is modified to improve its efficiency and extended to accommodate the multi-label recurrent-item associative-classification rule generation. The proposed algorithm is tested and compared with A priori-based associative-classification rule generator on two large datasets. IS - 1 JF - Data & Knowledge Engineering EP - 197 TI - A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation VL - 64 T2 - Fourth International Conference on Business Process Management (BPM 2006) - Four selected and extended papers; 8th International Conference on Enterprise Information Systems (ICEIS' 2006) - Three selected and extended papers SP - 171 KW - classification KW - freq_sets AU - Rak, Rafal AU - Kurgan, Lukasz AU - Reformat, Marek PY - 2008/01// UR - http://dx.doi.org/10.1016/j.datak.2007.05.006 ER -