• East Asian mathematical journal
• /
• v.25 no.4
• /
• pp.533-541
• /
• 2009

In this paper, we classify reflexible edge-transitive embeddings of complete bipartite graphs $K_{m,n}$ for any odd positive integers m and n. As a result, for any odd m, n, it will be shown that there exists only one reflexible edge-transitive embedding of $K_{m,n}$ up to isomorphism.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.409-414
• /
• 2013

Naive Bayesian classifiers are a powerful and well-known type of classifiers that can be easily induced from a dataset of sample cases. However, the strong conditional independence assumptions can sometimes lead to weak classification performance. Normally, naive Bayesian classifiers use Gaussian distributions to handle continuous attributes and to represent the likelihood of the features conditioned on the classes. The probability density of attributes, however, is not always well fitted by a Gaussian distribution. Another eminent type of classifier is the neuro-fuzzy classifier, which can learn fuzzy rules and fuzzy sets using supervised learning. Since there are specific structural similarities between a neuro-fuzzy classifier and a naive Bayesian classifier, the purpose of this study is to apply learning distribution graphs constructed by a neuro-fuzzy network to naive Bayesian classifiers. We compare the Gaussian distribution graphs with the fuzzy distribution graphs for the naive Bayesian classifier. We applied these two types of distribution graphs to classify leukemia and colon DNA microarray data sets. The results demonstrate that a naive Bayesian classifier with fuzzy distribution graphs is more reliable than that with Gaussian distribution graphs.

• Journal of Korea Entertainment Industry Association
• /
• v.14 no.4
• /
• pp.349-354
• /
• 2020

This paper, firstly, propose a new Clustering scheme Based on Conceptual graphs (CBC) that can describe objects freely and can perform clustering efficiently. The conceptual clustering is one of machine learning technique. The similarity among the objects in conceptual clustering are decided on the bases of concept membership, unlike the general clustering scheme which decide the similarity without considering the context or environment of the objects. A new conceptual clustering scheme, CBC, which can perform efficient conceptual clustering by describing various objects freely with conceptual graphs is introduced in this paper.

• Journal of the Korean Mathematical Society
• /
• v.54 no.1
• /
• pp.227-248
• /
• 2017

In this paper, we reduce the classification problem of equivariant (topological complex) vector bundles over a simple graph to the classification problem of their isotropy representations at vertices and midpoints of edges. Then, we solve the reduced problem in the case when the simple graph is homeomorphic to a circle. So, the paper could be considered as a generalization of [3].

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.11
• /
• pp.1571-1576
• /
• 2022

This study aims to analyze the interest of modern people in non-face-to-face medical counseling in the medical industrys. Big data was collected on two social platforms, 지식인, a platform that allows experts to receive medical counseling, and YouTube. In addition to the top five keywords of telephone counseling, "internal medicine", "general medicine", "department of neurology", "department of mental health", and "pediatrics", a data set was built from each platform with a total of eight search terms: "specialist", "medical counseling", and "health information". Afterwards, pre-processing processes such as morpheme classification, disease extraction, and normalization were performed based on the crawled data. Data was visualized with word clouds, broken line graphs, quarterly graphs, and bar graphs by disease frequency based on word frequency. An emotional classification model was constructed only for YouTube data, and the performance of GRU and BERT-based models was compared.

• The Korean Journal of Applied Statistics
• /
• v.33 no.3
• /
• pp.309-320
• /
• 2020

Bayesian networks, also known as directed acyclic graphs (DAG), are used in many areas of medicine, meteorology, and genetics because relationships between variables can be modeled with graphs and probabilities. In particular, Bayesian network classifiers, which are used to predict discrete data, have recently become a new method of data mining. Bayesian networks can be grouped into different models that depend on structured learning methods. In this study, Bayesian network models are learned with various properties of structure learning. The models are compared to the simplest method, the naïve Bayes model. Classification results are compared by applying learned models to various real data. This study also compares the relationships between variables in the data through graphs that appear in each model.

• Journal of the Korean housing association
• /
• v.19 no.2
• /
• pp.43-54
• /
• 2008

The aim of the research is to find out the direction of unit-plans in multi-dwellings for the future society. Shinonome Canal Court where residents actually live now are the objects in this study, and the residential floor plan of unit-plans were analyzed to find out the typical types. The analysis was focused on the unit-plans of 5 blocks of Shinonome Canal Court. Space Syntax Theory was used as the analysis method. As the first stage of the analysis, justified graphs were made to find out the characters of unit-plans through the classification of the graphs. Contents of the analysis are as follows: Relationship between classified justified graphs and dimension according to node number. Relationship between classified justified graph patterns and unit-plans. Characters of unit-plans in each blocks. Shinonome Canal Court consists of mainly small scale unit-plans and 30unit-plans are classified. 1LDK, 2LDK, 1LDK+S, 1LDK+f are typical unit-plans which are mainly supplied in the complex. It is noted that the results of the analysis by node, justified graph pattern and dimension are the same. It also presents diverse unit-plans which shows a change in nLDK pattern or add f (foyer), AN (annex), S (service room), Fs (free space) to basic nLDK type. In summary, it demonstrates the possibility of creating new residental floor plans in multi-dwellings.

• International journal of advanced smart convergence
• /
• v.12 no.2
• /
• pp.34-46
• /
• 2023

A growing number of large scale knowledge graphs raises several issues how knowledge graph data can be organized, discovered, and integrated efficiently. We present a novel semantic-based mashup platform for contents convergence which consists of acquisition, RDF storage, ontology learning, and mashup subsystems. This platform servers a basis for developing other more sophisticated applications required in the area of knowledge big data. Moreover, this paper proposes an entity matching method using graph convolutional network techniques as a preliminary work for automatic classification and discovery on knowledge big data. Using real DBP15K and SRPRS datasets, the performance of our method is compared with some existing entity matching methods. The experimental results show that the proposed method outperforms existing methods due to its ability to increase accuracy and reduce training time.

• Korean Journal of Remote Sensing
• /
• v.27 no.6
• /
• pp.717-728
• /
• 2011

An efficient method for the unsupervised classification of high resolution imagery is suggested in this paper. It employs pixel-linking and merging based on the adjacency graph. The proposed algorithm uses the neighbor lines of 8 directions to include information in spatial proximity. Two approaches are suggested to employ neighbor lines in the linking. One is to compute the dissimilarity measure for the pixel-linking using information from the best lines with the smallest non. The other is to select the best directions for the dissimilarity measure by comparing the non-homogeneity of each line in the same direction of two adjacent pixels. The resultant partition of pixel-linking is segmented and classified by the merging based on the regional and spectral adjacency graphs. This study performed extensive experiments using simulation data and a real high resolution data of IKONOS. The experimental results show that the new approach proposed in this study is quite effective to provide segments of high quality for object-based analysis and proper land-cover map for high resolution imagery of urban area.

• The Journal of Korean Academy of Prosthodontics
• /
• v.38 no.6
• /
• pp.814-820
• /
• 2000

Statement of Problem. Commonly used classification systems do not inform for dentists the dimension of the available bone at a potential implant site although regarding a variety of morphologic and pathophysiologic aspects using schematic graphs. However, for the implantologist the availability of bone substance is most important independent whether it concerns the jaw basis or the alveolus. Purpose of Study. The present article refers to a new evaluation form, to analyze the available bone with regard to optional immediate loading site by site. According to a new systematic implant planning concept will be presented in two case reports. Results. The feasibility of the classification for planning and documentation of immediately loaded implants is presented in two case reports. Conclusion. The factor of bone support for immediate functional stability is important in dental implantology. The new systematic implant planning helps to systematically estimate the dimension (ASCIi classification) of the alveolus site by site to evaluate the possibility of immediate loading. The Gottingen classification thus aids to determine the degree of stability that can be expected for the planned solution.