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A Two-Stage Approach for Constructing Type-2 Information Granules.
- Source :
-
IEEE transactions on cybernetics [IEEE Trans Cybern] 2022 Apr; Vol. 52 (4), pp. 2214-2224. Date of Electronic Publication: 2022 Apr 05. - Publication Year :
- 2022
-
Abstract
- In this article, we are concerned with the formation of type-2 information granules in a two-stage approach. We present a comprehensive algorithmic framework which gives rise to information granules of a higher type (type-2, to be specific) such that the key structure of the local granular data, their topologies, and their diversities become fully reflected and quantified. In contrast to traditional collaborative clustering where local structures (information granules) are obtained by running algorithms on the local datasets and communicating findings across sites, we propose a way of characterizing granular data (formed) by forming a suite of higher type information granules to reveal an overall structure of a collection of locally available datasets. Information granules built at the lower level on a basis of local sources of data are weighted by the number of data they represent while the information granules formed at the higher level of hierarchy are more abstract and general, thus facilitating a formation of a hierarchical description of data realized at different levels of detail. The construction of information granules is completed by resorting to fuzzy clustering algorithms (more specifically, the well-known Fuzzy C-Means). In the formation of information granules, we follow the fundamental principle of granular computing, viz., the principle of justifiable granularity. Experimental studies concerning selected publicly available machine-learning datasets are reported.
- Subjects :
- Cluster Analysis
Algorithms
Pattern Recognition, Automated
Subjects
Details
- Language :
- English
- ISSN :
- 2168-2275
- Volume :
- 52
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- IEEE transactions on cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 32721903
- Full Text :
- https://doi.org/10.1109/TCYB.2020.2965967