caa a22 4500 605469709 CHVBK 20210128100323.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s00500-014-1439-x doi (NATIONALLICENCE)springer-10.1007/s00500-014-1439-x Distributed proximity-based granular clustering: towards a development of global structural relationships in data [Elektronische Daten] [Witold Pedrycz, Rami Al-Hmouz, Ali Morfeq, Abdullah Balamash] The study is focused on a development of a global structure in a family of distributed data realized on a basis of locally discovered structures. The local structures are revealed by running fuzzy clustering (Fuzzy C-Means), whereas building a global view is realized by forming global proximity matrices on a basis of the local proximity matrices implied by the partition matrices formed for the individual data sets. To capture the diversity of local structures, a global perspective at the structure of the data is captured in terms of a granular proximity matrix, which is built by invoking a principle of justifiable granularity with regard to the aggregation of individual proximity matrices. The three main scenarios are investigated: (a) designing a global structure among the data through building a granular proximity matrix, (b) refining a local structure (expressed in the form of a partition matrix) by engaging structural knowledge conveyed at the higher level of the hierarchy and provided in the form of the granular proximity matrix, (c) forming a consensus-building scheme and updating all local structures with the aid of the proximity dependences available at the upper layer of the hierarchy. While the first scenario delivers a passive approach to the development of the global structure, the two others are of an active nature by facilitating a structural feedback between the local and global level of the hierarchy of the developed structures. The study is illustrated through a series of experiments carried out for synthetic and publicly available data sets. The Author(s), 2014 Fuzzy clustering nationallicence Proximity matrix nationallicence Global structure nationallicence Granular proximity nationallicence Granular clustering nationallicence Distributed data nationallicence Consensus formation nationallicence Pedrycz Witold Department of Electrical and Computer Engineering, University of Alberta, T6R 2V4, Edmonton, AB, Canada aut Al-Hmouz Rami Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, 21589, Jeddah, Saudi Arabia aut Morfeq Ali Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, 21589, Jeddah, Saudi Arabia aut Balamash Abdullah Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, 21589, Jeddah, Saudi Arabia aut Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2751-2767 1432-7643 19:10<2751 2015 19 500 https://doi.org/10.1007/s00500-014-1439-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1439-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Pedrycz Witold Department of Electrical and Computer Engineering, University of Alberta, T6R 2V4, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Al-Hmouz Rami Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, 21589, Jeddah, Saudi Arabia aut NATIONALLICENCE 700 1- Morfeq Ali Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, 21589, Jeddah, Saudi Arabia aut NATIONALLICENCE 700 1- Balamash Abdullah Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, 21589, Jeddah, Saudi Arabia aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/10(2015-10-01), 2751-2767 1432-7643 19:10<2751 2015 19 500