Collaborative Evidential Clustering
| العنوان: | Collaborative Evidential Clustering |
|---|---|
| المؤلفون: | Shoumei Li, Yixuan Qiao, Thierry Denœux |
| المساهمون: | Beijing University of Technology, Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS) |
| المصدر: | Advances in Intelligent Systems and Computing ISBN: 9783030219192 Fuzzy Techniques: Theory and Applications-International Fuzzy Systems Association World Congress (IFSA/NAFIPS'2019) Fuzzy Techniques: Theory and Applications-International Fuzzy Systems Association World Congress (IFSA/NAFIPS'2019), Jun 2019, Louisiana, United States. pp.518-530, ⟨10.1007/978-3-030-21920-8_46⟩ |
| بيانات النشر: | Springer International Publishing, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Key point, Information retrieval, Computer science, 0206 medical engineering, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Confidentiality, 02 engineering and technology, Cluster analysis, 020601 biomedical engineering, Partition (database), ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| الوصف: | Different companies may not be allowed to treat data together given restrictions of security, privacy or other technical reasons. In order to make better use of information from different sources, clustering algorithms based on collaboration mechanisms have been widely used. We propose the concept of collaborative evidential clustering under the framework of evidence theory. The key point is to establish collaboration among the credal partition matrices of each data site to meet the data confidentiality requirements. Considering the problems of excessive information interaction and insufficient information interaction, we design single-step and multi-step collaborative evidential clustering algorithms. Our algorithms were validated on real data sets. |
| ردمك: | 978-3-030-21919-2 |
| DOI: | 10.1007/978-3-030-21920-8_46 |
| DOI: | 10.1007/978-3-030-21920-8_46⟩ |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e935f18df1c68f51cf056832ccad6073 https://doi.org/10.1007/978-3-030-21920-8_46 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e935f18df1c68f51cf056832ccad6073 |
| قاعدة البيانات: | OpenAIRE |
| ردمك: | 9783030219192 |
|---|---|
| DOI: | 10.1007/978-3-030-21920-8_46 |