• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.432-436
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• 2010

Finding communities from network data including social networks can be done by clustering the nodes of the network as densely interconnected groups, where keeping interconnection between groups sparse. To exploit a clustering algorithm for community detection task, we need a well-defined similarity measure between network nodes. In this paper, we propose a new similarity measure named "Common Neighborhood Sub-graph density" and combine the similarity with affinity propagation, which is a recently devised clustering algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.168-171
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• 2011

One important issue in semantic web is identification and selection of domain concepts for domain ontology learning when several hundreds or even thousands of terms are extracted and available from relevant text documents shared among the members of a domain. We present a novel domain concept acquisition and selection approach for ontology learning that uses affinity propagation algorithm, which takes as input semantic and structural similarity between pairs of extracted terms called data points. Real-valued messages are passed between data points (terms) until high quality set of exemplars (concepts) and cluster iteratively emerges. All exemplars will be considered as domain concepts for learning domain ontologies. Our empirical results show that our approach achieves high precision and recall in selection of domain concepts using less number of iterations.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.828-837
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• 2008

We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.339-342
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• 1988

Typical magnetic bubble propagation failure modes of ion implanted magnetic bubble computer memory devices were observed and their failure mechanisms were analize. The skidding failure mode is due to the pushing of a strong repulsive charged wall. If this pushing is stronger than the edge affinity of the bubble in the cusp, the bubble moves out of the cusp when it is supposed to stay there. The stripeout failure modes across the adjacent track or along the track can be explained by considering the relative strength of the charged wall and the edge affinity encountered by both ends of the stripe. The skipping of the first cusp of a track is believed to be due to the whipping motion of the charged wall. The bubble moves directly to the second cusp via the long charged wall pointing to the second cusp skipping the first cusp.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3050-3063
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• 2023

Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.31-32
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• 2012

본 연구는 소셜 네트워크에서 사용자간의 친밀도에 기반하여 보여주는 '친구추천' 이라는 방법을 그래프 클러스터링을 이용하여 접근하고자 한다. 기존의 방법과는 다르게 사용자에게 개인화된 선별 정보를 제공하는데 목적이 있다. 또한 일반적 클러스터링이 아닌 그래프 이론에 근거한 거리 계산을 기반으로 친화력 전파 모델(Affinity Propagation) 클러스터링 기법을 적용하는 방법을 제안한다. 이 방법으로 클러스터링을 진행하여 선별된 같은 그룹 안에 있는 개인화된 친구 추천을 효과적으로 수행할 수 있음을 입증하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1294-1297
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• 2011

최근, Flickr, YouTube 와 같은 사용자 참여형 미디어 공유 및 검색 사이트가 폭발적으로 증가하면서, 이를 멀티미디어 정보 검색 서비스에 효과적으로 활용하기 위한 다양한 연구들이 시도되고 있다. 특히, 이미지에 할당되어 있는 태그를 이용하여 이미지를 효과적으로 검색하기 위한 연구가 활발히 진행 중이다. 그러나 사용자들에 의해 제공되는 소셜 이미지들은 매우 다양한 범위와 주제를 가지고 있기 때문에, 소셜 이미지들의 분류 및 태그 할당을 위한 트레이닝 집합의 획득이 쉽지 않다는 한계점을 가지고 있다. 본 논문에서는 데이터 군집화를 위한 클러스터링 알고리즘들 중 K-Means, K-Medoids, Affinity Propagation 을 활용하여 소셜 이미지 집합으로부터 트레이닝 집합을 획득하기 위한 방법들을 살펴 본다. 또한, 각 알고리즘으로부터 획득한 트레이닝 집합을 이용하여 소셜 이미지를 분류한 결과를 비교 분석한다.

• Journal of Microbiology and Biotechnology
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• v.25 no.2
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• pp.196-205
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• 2015

A Sulfolobus-E. coli shuttle vector for an efficient expression of the target gene in S. acidocaldarius strain was constructed. The plasmid-based vector pSM21 and its derivative pSM21N were generated based on the pUC18 and Sulfolobus cryptic plasmid pRN1. They carried the S. solfataricus P2 pyrEF gene for the selection marker, a multiple cloning site (MCS) with C-terminal histidine tag, and a constitutive promoter of the S. acidocaldarius gdhA gene for strong expression of the target gene, as well as the pBR322 origin and ampicillin-resistant gene for E. coli propagation. The advantage of pSM21 over other Sulfolobus shuttle vectors is that it contains a MCS and a histidine tag for the simple and easy cloning of a target gene as well as one-step purification by histidine affinity chromatography. For successful expression of the foreign genes, two genes from archaeal origins (PH0193 and Ta0298) were cloned into pSM21N and the functional expression was examined by enzyme activity assay. The recombinant PH0193 was successfully expressed under the control of the gdhA promoter and purified from the cultures by His-tag affinity chromatography. The yield was approximately 1 mg of protein per liter of cultures. The enzyme activity measurements of PH0913 and Ta0298 revealed that both proteins were expressed as an active form in S. acidocaldarius. These results indicate that the pSM21N shuttle vector can be used for the functional expression of foreign archaeal genes that form insoluble aggregates in the E. coli system.

• 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.

• Korean Journal of Plant Resources
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• v.28 no.6
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• pp.710-717
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• 2015

Humankind has been searching for medicinal materials from various plant sources in an attempt to treat disease. Mistletoe is one indubitable plant source for these materials due to its effectiveness in treating various diseases, but it has almost disappeared from the mountainous areas of Korea due to excessive harvesting. In this study, in order to select host tree species for Korean mistletoe [Viscum album var. coloratum (Kom.) Ohwi] by seed inoculation and to clarify the effect of host specificity among various tree species were conducted for the purpose of gaining basic information for the artificial cultivation of Korean mistletoe. Almost all the seeds of Korean mistletoe germinated in vitro at the temperature of 15℃. Among host trees used in this study, Prunus mume showed the highest parasitic affinity with inoculated Korean mistletoe, compared with any other host plants. However, treatment of hormones could not increase the low survival rate of Korean mistletoe on the host trees.