• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.479-490
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• 2003

CBD is a software development approach based on reusable component and supports easy modification and evolution of software. For the success of this approach, a component must be developed with high cohesion and low coupling. In this paper, we propose the two types of clustering analysis technique based on affinity between use-cases and classes and propose component identification method applying to this technique. We also propose component reference model and CBD methodology framework and perform a ease study to demonstrate how the affinity-based clustering technique is used in component identification method. Component identification method contains three tasks such as component extraction, component specification and component architecting. This method uses object-oriented concept for identifying component, which improves traceability from analysis to implementation and can automatically extract component. This method reflects the low coupling-high cohesion principle for good modularization about reusable component.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.