• Journal of Platform Technology
• /
• v.11 no.5
• /
• pp.23-36
• /
• 2023

This study aims to analyze the similarities and dissimilarities of various versions of <Heungbu-jeon> at both micro- and macro-levels using contents analysis techniques and the Hamming distance metrics. The 28 versions of <Heungbu-jeon> were segmented into 341 content units, and for each unit, the value of the content type was encoded. The dissimilarities between content types were compared among all versions by the content unit, respectively. The (dis-)similarities based on the content types of the 28 versions were aggregated and transformed into a distance matrix. The matrix was interpreted by multi-dimensional scaling, resulting into the two-dimensional coordinates. By visualizing the results by multi-dimensional scaling analysis, it was confirmed that the versions of <Heungbu-jeon> can be broadly divided into two groups. Hierarchical clustering and phylogenetic analysis were applied to analyze the clusters of the 28 versions, using the same distance matrix. The results showed that there are five clusters based on the micro-level analysis of (dis-)similarities within two major clusters. This study demonstrated the usefulness of applying digital humanities methods to encode the content of classical literary versions and analyze the data using clustering analysis techniques based on the (dis-)similarity of literary content.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2003.10a
• /
• pp.170-177
• /
• 2003

Gene expression data are the quantitative measurements of expression levels and ratios of numberous genes in different situations based on microarray image analysis results. The process to draw meaningful information related to genomic diseases and various biological activities from gene expression data is known as gene expression data analysis. In this paper, we present a hierarchical clustering method of gene expression data based on self organizing map which can analyze the clustering result of gene expression data more efficiently. Using our proposed method, we could eliminate the uncertainty of cluster boundary which is the inherited disadvantage of self organizing map and use the visualization function of hierarchical clustering. And, we could process massive data using fast processing speed of self organizing map and interpret the clustering result of self organizing map more efficiently and user-friendly. To verify the efficiency of our proposed algorithm, we performed tests with following 3 data sets, animal feature data set, yeast gene expression data and leukemia gene expression data set. The result demonstrated the feasibility and utility of the proposed clustering algorithm.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.10
• /
• pp.761-765
• /
• 2009

This research aims an optimal placement of Mobile Base Station (MBS) under HAP based network configurations with the restrictions of HAP capabilities. With clustering algorithm based on BIRCH, mobile ground nodes are clustered and the centroid of the clusters will be the location of MBS. The hierarchical structure of BIRCH enables mobile node management by CF tree and the restrictions of maximum nodes per MBS and maximum radio coverage are accomplished by splitting and merging clusters. Mobility models based on Jeju island are used for simulations and such restrictions are met with proper placement of MBS.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.399-400
• /
• 2009

As a part of the era of human centric applications people started to care about their well being utilizing any possible mean. This paper proposes a framework for real time on-body sensor health-care system, addresses the current issues in such systems, and utilizes an enhanced online divisive agglomerative clustering algorithm (EODAC); an algorithm that builds a top-down tree-like structure of clusters that evolves with streaming data to rationally cluster on-body sensor data and give accurate diagnoses remotely, guaranteeing high performance, and scalability. Furthermore it does not depend on the number of data points.

• Journal of KIISE:Databases
• /
• v.31 no.1
• /
• pp.13-22
• /
• 2004

With the proliferation of multimedia data, there is an increasing need to support the indexing and retrieval of high-dimensional image data. Although there have been many efforts, the performance of existing multidimensional indexing methods is not satisfactory in high dimensions. Thus the dimensionality reduction and the approximate solution methods were tried to deal with the so-called dimensionality curse. But these methods are inevitably accompanied by the loss of precision of query results. Therefore, recently, the vector approximation-based methods such as the VA- file and the LPC-file were developed to preserve the precision of query results. However, the performance of the vector approximation-based methods depend largely on the size of the approximation file and they lose the advantages of the multidimensional indexing methods that prune much search space. In this paper, we propose a new index structure called the GC-tree for efficient similarity search in image databases. The GC-tree is based on a special subspace partitioning strategy which is optimized for clustered high-dimensional images. It adaptively partitions the data space based on a density function and dynamically constructs an index structure. The resultant index structure adapts well to the strongly clustered distribution of high-dimensional images.

• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.3
• /
• pp.37-46
• /
• 2013

Similarity measure of catchments is essential for regionalization studies, which provide in depth analysis in hydrological response and flood estimations at ungauged catchments. However, this similarity measure is often biased to the selected catchments and is not clearly explained in hydrological sense. This study applied a type of hydrological similarity distance measure-Flood Estimation Handbook to 25 Geum River catchments, Korea. Three Catchment Characteristics, Area(A)-Annual precipitation(SAAR)-SCS Curve Number(CN), are used in Euclidian distance measures. Furthermore, six index of Flow Duration Curve are applied to clustering analysis of SPSS. The catchments' grouping of hydrological similarity measures suggests three groups (H1, H2 and H3) and the four catchments are not grouped in this study. The clustering analysis of FDC provides four Groups; F1, F2, F3 and F4. The six catchments (out of seven) of H1 are grouped in F1, while Sangyeogyo is grouped in F2. The four catchments (out of six) of H2 are also grouped in F2, while Cheongju and Guryong are grouped in F1. The catchments of H3 are categorized in F1. The authors examine the results (H1, H2 and H3) of similarity measure based on catchment physical descriptors with results (F1 and F2) of clustering based on catchment hydrological response. The results of hydrological similarity measures are supported by clustering analysis of FDC. This study shows a potential of hydrological catchment similarity measures in Korea.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2014.01a
• /
• pp.281-282
• /
• 2014

병의원에서 다양한 정보시스템을 도입하면서 환자들과 관련된 방대한 의료 데이터들이 전자적인 형태로 축적되어 왔고, 최근에는 의료진이나 환자에게 적절한 정보를 제공하는데 이러한 데이터를 활용하고자 하는 노력이 이어지고 있다. 그러나 의료 데이터는 분량이 방대하고 전문적인 내용을 다루기 때문에 이에 기반한 정보를 개인 환자에게 제공하는데 있어서는 데이터에 포함된 내용을 사용자의 이해가 편리한 형태로 가공하는 것이 중요하다. 이에 본 논문에서는 연관분석과 관련된 행렬 기반 표현 방법을 기반으로 한 하이브리드 시각화 방법을 개발하고, 이를 건강검진 결과에 적용하는 것을 제안하고자 한다.

• International Commerce and Information Review
• /
• v.14 no.3
• /
• pp.539-558
• /
• 2012

The purpose of this study is examining the differences of mobile SNS's service quality, which consists data quality and system quality, among the groups that are classified by commitment and customer loyalty. For the experimental analysis, the frequency analysis was performed for general characteristics of sample. The variables were selected by factor analysis that also prove the validity of variables. The value of Cronbach's alpha was calculated to check the reliability of variables. In addition, the group was determined by the both hierarchical and hierarchical cluster analysis, then ANOVA was performed to test the hypotheses that there are differences of mobile SNS's service quality, among the groups that are classified by commitment and customer loyalty. The results of this study support that there are differences among the groups toward mobile SNS's service quality and also shows the more commitment and loyalty group is the higher recognition of mobile SNS's service quality. Thus, the companies have to realize that mobile SNS is very important key factor to success in rapidly changing business environment. In conclusion, the companies implement different customized strategy for the different group and develop the contents and the applications to maximize the commitment and loyalty of for the mobile SNS users.

• Journal of KIISE:Databases
• /
• v.37 no.3
• /
• pp.127-136
• /
• 2010

With rapid growth of information and computer communication technologies, the numbers of digital documents including multimedia data have been recently exploded. In particular, news video database and news video mining have became the subject of extensive research, to develop effective and efficient tools for manipulation and analysis of news videos, because of their information richness. However, many research focus on browsing, retrieval and summarization of news videos. Up to date, it is a relatively early state to discover and to analyse the plentiful latent semantic knowledge from news videos. In this paper, we propose the news video mining system based on multi-modal approach and text mining, which uses the visual-textual information of news video clips and their scripts. The proposed system systematically constructs a taxonomy of news video stories in automatic manner with hierarchical clustering algorithm which is one of text mining methods. Then, it multilaterally analyzes the topics of news video stories by means of time-cluster trend graph, weighted cluster growth index, and network analysis. To clarify the validity of our approach, we analyzed the news videos on "The Second Summit of South and North Korea in 2007".

• Journal of the Korea Institute of Building Construction
• /
• v.19 no.4
• /
• pp.341-350
• /
• 2019

Construction industry is one of the most dangerous industry. As the construction accidents occur due to the repeated factors found in each accidents, there is a limitation in analyzing all types of occupational accidents by the existing descriptive analysis and statistical test. In this study, we classified safety accidents caused by falling objects among the accident types occurring at construction sites into fatal and nonfatal accidents and deduced the factors. In addition, we deduced the association rules among the safety accidents factors caused by falling objects through the association rule analysis method among the machine learning techniques. Therefore, considering the association rules for fatal and nonfatal accidents proposed in this study, it would be possible to prevent accidents by searching for countermeasures against safety accidents caused by falling objects.