A Design of CGK-Based Granular Model Using Hierarchical Structure
Source abstract
Abstract
In this paper, we propose context-based GK clustering and design a CGK-based granular model and a hierarchical CGK-based granular model. Existing fuzzy clustering generates clusters using Euclidean distances. However, there is a problem in that performance decreases when a cluster is created from data with strong nonlinearity. To improve this problem, GK clustering is used. GK clustering creates clusters using Mahalanobis distance. In this paper, we propose context-based GK (CGK) clustering, which adds a method that considers the output space in the existing GK clustering, to create a cluster that considers not only the input space but also the output space. there is. Based on the proposed CGK clustering, a CGK-based granular model and a hierarchical CGK-based granular model were designed. Since the output of the CGK-based granular model is in the form of a context, it has the advantage of verbally expressing the prediction result, and the CGK-based granular model with a hierarchical structure can generate high-dimensional information granules, so meaningful information with high abstraction value granules can be created. In order to verify the validity of the method proposed in this paper, as a result of conducting an experiment using the concrete compressive strength database, it was confirmed that the proposed methods showed superior performance than the existing granular models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Clustering Algorithms Research · Text and Document Classification Technologies · Rough Sets and Fuzzy Logic
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for IT-Bio Convergence System Agriculture, Chonnam University, Gwangju 61186, Korea
Department of Electronics Engineering IT-Bio Convergence System, Chosun University, Gwangju 61452, Korea
Funding
NRF
References (58)
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