• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6084-6091
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• 2014

This paper presents the GJR GARCH model (Glosten et. al, 1993) to analyze the influences of volume volatilities on price volatilities in the fishery market. For the analysis, this study used the monthly price and volume data of aquacultural flatfish in Jeju. As a result, empirical analysis suggested volatility clustering. The persistency parameter(${\lambda}$) was estimated to be approximately 1 in aquacultural flatfish. The results showed that there is a significant negative relationship between the conditional variance of supply and that of price for aquacultural flatfish. This means that the general law of supply is valid. Finally, the empirical analysis was that an asymmetric coefficient (${\gamma}$) of GJR GARCH model was negative (-). This means that the higher volatility of volume leads to lower price volatility. That is, it is useful to make government policies that can adjust the volume (stockpiling, stabilizing supply and demand).

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.63-81
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• 2023

This study endeavors to classify and categorize similar policy programs through network clustering analysis, using textual information from data-related policy programs in Korea. To achieve this, descriptions of data-related budgetary programs in South Korea in 2022 were collected, and keywords from the program contents were extracted. Subsequently, the similarity between each program was derived using TF-IDF, and policy program network was constructed accordingly. Following this, the structural characteristics of the network were analyzed, and similar policy programs were clustered and categorized through network clustering. Upon analyzing a total of 97 programs, 7 major clusters were identified, signifying that programs with analogous themes or objectives were categorized based on application area or services utilizing data. The findings of this research illuminate the current status of data-related policy programs in Korea, providing policy implications for a strategic approach to planning future national data strategies and programs, and contributing to the establishment of evidence-based policies.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1147-1161
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• 2015

Nowadays we are considering and analyzing not only classical data expressed by points in the p-dimensional Euclidean space but also new types of data such as signals, functions, images, and shapes, etc. Symbolic data also can be considered as one of those new types of data. Symbolic data can have various formats such as intervals, histograms, lists, tables, distributions, models, and the like. Up to date, symbolic data studies have mainly focused on individual formats of symbolic data. In this study, it is extended into datasets with both histogram and multimodal-valued data and a divisive clustering method for the mixed feature-type symbolic data is introduced and it is applied to the analysis of industrial accident data.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.1
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• pp.1-11
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• 2020

Wireless sensor networks consist of a large number of tiny sensor nodes which have limited battery life. In order to maximize the network life span, we propose an optimal transmission radius, R, for control messages. We analyze the transmission radius as a function of the energy consumption of cluster head nodes and the energy consumption of member nodes to find the optimal value of R. In simulations we apply our proposed optimization of transmission range to LEACH-based single-hop and multi-hop networks to show that our proposed scheme outperforms other existing routing algorithms in terms of network life span.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.26-31
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• 2005

In this paper, we present the document summarizers which are simpler and more condense than the existing ones generally used in the web search engines. This method is a statistic-based summarization method using the concept of the complete graph. We suppose that each sentence as a vertex and the similarity between two sentences as a link of the graph. We compare this summarizer with those of Clustering and MMR techniques which are well-known as the good summarization methods. For the comparison, we use FScore using the summarization results generated by human subjects. Our experimental results verify the accuracy of this method, being about $30\%$ better than the others.

• Industrial Engineering and Management Systems
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• v.11 no.4
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• pp.371-384
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• 2012

This study proposes a multi-level data analysis approach to identify both superficial and latent relationships among variables in the data set obtained from a vocational rehabilitation (VR) services program of people with significant disabilities. At the first layer, data mining and statistical predictive models are used to extract the superficial relationships between dependent and independent variables. To supplement the findings and relationships from the analysis at the first layer, association rule mining algorithms at the second layer are employed to extract additional sets of interesting associative relationships among variables. Finally, nonlinear nonparametric canonical correlation analysis (NLCCA) along with clustering algorithm is employed to identify latent nonlinear relationships. Experimental outputs validate the usefulness of the proposed approach. In particular, the identified latent relationship indicates that disability types (i.e., physical and mental) and severity (i.e., severe, most severe, not severe) have a significant impact on the levels of self-esteem and self-confidence of people with disabilities. The identified superficial and latent relationships can be used to train education program designers and policy developers to maximize the outcomes of VR training programs.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.47-55
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• 2008

The purpose of this paper is to develop an analytical methodology of or릴 documents by the application of. Information Technologies. This system consists of the key word search, contents summary, clustering, classification & topic tracing of the contents. The integrated model of the five levels of retrieval technologies can be exhaustively used in the analysis of oral documents, which were collected as oral history of five men and women in the area of North Jeolla. Of the five methods topic tracing is the most pioneering accomplishment both home and abroad. In final this research will shed light on the methodological and theoretical studies of oral history and culture.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.11
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• pp.4553-4562
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• 2010

With developing networks, information security is going to be important and therefore lots of intrusion detection system has been developed. Intrusion detection system has abilities to detect abnormal behavior and unknown intrusions also it can detect intrusions by using patterns studied from various penetration methods. Various algorithms are studying now such as the statistical method for detecting abnormal behavior, extracting abnormal behavior, and developing patterns that can be expected. Etc. This study using clustering of data mining and association rule analyzes detecting areas based on two models and helps design detection system which detecting abnormal behavior, unknown attack, misuse attack in a large network.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.3
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• pp.163-173
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• 2024

This study aims to establish an online shopping mall marketing strategy based on big data analysis methods. The customer cluster analysis method was utilized to analyze customer purchase patterns and segment them into customer groups with similar characteristics. Data was collected from orders placed over one year in 2023 at 'Jeonbuk Saengsaeng Market', the official online shopping mall for agricultural, fish, and livestock products of Jeonbuk Special Self-Governing Province. K-means clustering was conducted by creating variables such as 'TotalPrice' and 'ElapsedDays' for analysis. The study identified four customer groups, and their main characteristics. Furthermore, regions corresponding to customer groups were analyzed using pivot tables. This facilitated the proposal of a marketing strategy tailored to each group's characteristics and the establishment of an efficient online shopping mall marketing strategy. This study is significant as it departs from the traditional reliance on the intuition of the person in charge to operate a shopping mall, instead establishing a shopping mall marketing strategy through objective and scientific big data analysis. The implementation of the marketing strategy outlined in this study is expected to enhance customer satisfaction and boost sales.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.72-77
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• 2005

Orthologs are genes having the same function across different species that specialize from a single gene in the last common ancestor of these species. Orthologous groups are useful in the genome annotation, studies on gene evolution, and comparative genomics. However, the construction of an orthologous group is difficult to automate and it takes so much time. It is also hard to guarantee the accuracy of the constructed orthologous groups. We propose a system to construct orthologous groups on many genomes automatically and rapidly. We utilize the grid computing to reduce the sequence alignment time, and we use clustering algorithm in the application of database to automate whole processes. We have generated orthologous groups for 20 complete prokaryotes genomes just in a day because of the grid computing. Furthermore, new genomes can be accommodated easily by the clustering algorithm and grid computing. We compared the generated orthologous groups with COGs (Clusters of orthologous Group of proteins) and KO (KEGG Ortholog). The comparison shows about 85 percent similarity compared with previous well-known orthologous databases.