Novel techniques and an efficient algorithm for closed pattern mining |
| |
| Affiliation: | 1. University of Pannonia, Department of Process Engineering, P.O. Box 158, Veszpreém H-8200, Hungary;2. The Finnish Microarray and Sequencing Centre, Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Tykistökatu 6A, 20520 Turku, Finland;1. Instituto para el Desarrollo Tecnológico y la Innovación en Comunicaciones (IDeTIC), Universidad de Las Palmas de Gran Canaria, Despacho D-102, Pabellón B, Ed. de Eletrónica y Comunicaciones, Campus de Tafira, 35017 Las Palmas, Spain;2. Escuela de Biología, Universidad de Costa Rica, Costa Rica;3. Systems Engineering and Automation Department, Universidad del País Vasco/Euskal Herriko Unibertsitatea, Spain;1. Informatics Center — Federal University of Pernambuco (UFPE), Pernambuco, Brazil;2. Federal University of Bahia (UFBA), Bahia, Brazil;1. Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), São José do Rio Preto, SP, Brazil;2. Faculty of Computation (FACOM), Federal University of Uberlândia (UFU), Uberlândia, MG, Brazil;3. Center of Mathematics, Computing and Cognition, Federal University of ABC (UFABC), Santo André, SP, Brazil;4. Federal Institute of Triângulo Mineiro (IFTM), Ituiutaba, MG, Brazil;5. Transdisciplinary Center for Study of Chaos and Complexity (NUTECC), São José do Rio Preto Medical School, São José do Rio Preto, SP, Brazil;6. Kidney Transplant Surgical Service, Base Hospital, Fundação Faculdade Regional de Medicina (FUNFARME), São José do Rio Preto, SP, Brazil;7. Pathologic Anatomy Service, Base Hospital, Fundação Faculdade Regional de Medicina (FUNFARME), São José do Rio Preto, SP, Brazil;1. Computer Science Department, Federal University of Maranhão (UFMA), São Luís, MA, Brazil;2. Department of Informatics, University of Minho, Braga, Portugal;1. Khalifa University of Science, Technology and Research, P.O. Box 127788, Abu Dhabi, United Arab Emirates;2. Etisalat BT Innovation Center, P.O. Box 127788, Abu Dhabi, United Arab Emirates;1. Faculty of Computer Science, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, D-39106 Magdeburg, Germany;2. Institute for Community Medicine, Ernst-Moritz-Arndt University Greifswald, Walther-Rathenau-Straße 48, D-17475 Greifswald, Germany;3. Institute for Diagnostic Radiology and Neuroradiology, Ernst-Moritz-Arndt University Greifswald, Sauerbruchstraße, D-17487 Greifswald, Germany |
| |
| Abstract: | In this paper we show that frequent closed itemset mining and biclustering, the two most prominent application fields in pattern discovery, can be reduced to the same problem when dealing with binary (0–1) data. FCPMiner, a new powerful pattern mining method, is then introduced to mine such data efficiently. The uniqueness of the proposed method is its extendibility to non-binary data. The mining method is coupled with a novel visualization technique and a pattern aggregation method to detect the most meaningful, non-overlapping patterns. The proposed methods are rigorously tested on both synthetic and real data sets. |
| |
| Keywords: | Biclustering Closed frequent itemset mining Clustering visualization Data mining algorithm Pattern detection |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
