• Journal of Korean Institute of Industrial Engineers
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• v.19 no.1
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• pp.19-29
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• 1993

The problem of clustering stock-keeping-units to assign storage locations is treated. Firstly, a construction heuristic algorithm is developed to cluster items considering demand dependencies(correlated assignment) for the case that the maximum number or the maximum volume(weight) of items per tray is constrained by the capacity of tray. Secondly, inventory-related cost as well as material handling cost is considered to determine the space requirement and the storage location of each item simultaneously.

• Bioinformatics and Biosystems
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• v.2 no.2
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• pp.62-65
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• 2007

We present here the strategy of data integration for inference of genetic regulatory modules. First, we construct all possible combinations of regulators of genes using chromatin-immunoprecipitation(ChIP)-chip data. Second, hierarchical clustering method is employed to analyze mRNA expression profiles. Third, integration method is applied to both of the data. Finally, we construct a genetic regulatory module which is involved in the function of ribosomal protein synthesis.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.389-390
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• 2019

Tensor decomposition, proven to be an efficient data processing method, can be used to provide data-driven services. we propose a novel datadriven mutation strategy for parent individuals selection, namely tensor-based DE with parapatric and cross-generation(TPCDE).

• World Technopolis Review
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• v.5 no.2
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• pp.98-107
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• 2016

By highlighting the efficiencies gained from regional specialization, the cluster concept has distracted economic development officials from their traditional role of diversifying regional and local economies. Clustering was a viable strategy for much of the 18 years following its original appearance in the literature. Now, two events cast doubt on the continued viability of cluster-based specialization. First, the digital convergence has blurred the boundaries that once separated one industry from another. An industry cluster strategy becomes difficult when the industry cannot be defined. Second, many cluster initiatives fail. Combining literature search with the system-theoretic notions of efficiency and redundancy, we find many factors moderate cluster success. This implies regions facing uncertain success in their cluster-building efforts should thoroughly understand their unique circumstances and build upon them. Regions with successful clusters are advised to aim for multiple related clusters or superclusters.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.831-844
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• 2008

Since cooperatives consist of many distinct members in the management environment and characteristics, it is necessary to make similar cooperatives into a few groups for the effective risk management of cooperatives' mutuality bank. This paper is a priori research for suggesting a guidance for effective risk management of cooperatives with different management strategy. For such purpose, we propose a way to group the members of cooperative's mutuality bank. The 30 continuous variables which is relative to cooperatives' management status are considered and six factors are extracted from those variables through factor analysis with empirical consideration to avoid wrong grouping and to enhance the practical interpretation. Based on extracted six factors and additional 3 categorical variables, six representative groups are derived by the two step clustering analysis. These findings are useful to execute a discriminatory risk management and other management strategy for a mutuality bank and others.

• Asian Journal of Innovation and Policy
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• v.5 no.3
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• pp.293-314
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• 2016

Patent information as a proxy measure of technological capability has been utilized to establish technological strategies of firms. It is important to monitor what competitors' plans for direction on research and development in the initial stage of new industry. Cancer metabolism has been considered as a beacon of hope for cancer research because it is anticipated that the research field will play a central role in developing effective cancer therapies. There is little attention given to understanding the status quo of organizational configurations. By utilizing network analysis, six sub-groups of cancer metabolism were categorized and the relationship between an individual field and participants were analyzed based on cluster and entire network-level. Although the largest drug and biotech companies tried to take an initiative across the whole fields, the differences in technological capabilities between them was discovered. This paper attempts to improve the validity of the suggested procedure and is significant in that it looks at the entire structure of cancer metabolism research from a strategic perspective for the first time.

• Journal of Korean Library and Information Science Society
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• v.31 no.2
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• pp.279-302
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• 2000

This study is to develop a new automatic system for the Korean thesaurus browser by which we can automatically control all the processes of searching queries such as, representation, generation, extension and construction of searching strategy and feedback searching. The system in this study is programmed by Delphi 4.0(PASCAL) and consists of database system, automatic indexing, clustering technique, establishing and expressing thesaurus, and automatic information retrieval technique. The results proved by this system are as follows: 1)By using the new automatic thesaurus browser developed by the new algorithm, we can perform information retrieval, automatic indexing, clustering technique, establishing and expressing thesaurus, information retrieval technique, and retrieval feedback. Thus it turns out that even the beginner user can easily access special terms about the field of a specific subject. 2) The thesaurus browser in this paper has such merits as the easiness of establishing, the convenience of using, and the good results of information retrieval in terms of the rate of speed, degree, and regeneration. Thus, it t m out very pragmatic.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.147-158
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• 2021

Due to COVID-19, the risk of price volatility in commodity and equity markets increases. The research and application of hedging is the most effective way to reduce the market risk. Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. We use K-means and hierarchical clustering methods to cluster companies and futures products respectively, and analyze the relationship between the number of hedging firms, regional distribution, nature of firms, capital distribution, company size, profitability, number of local Futures Commission Merchants (FCMs), regional location, and listing time. The study shows that listed companies with large scale and good profitability invest more money in hedging, while state-owned enterprises' participation in hedging is more likely to be affected by the company size and the number of local futures commission merchants, and private enterprises are more likely to be affected by the company profitability and the regional location. Listed companies are more willing to choose long-listed and mature futures products for hedging. We also provide policy advice based on our conclusion. So far, there is no study on the characteristics of hedging. This paper fills the gap. The results provide a basis and guidance for people's investment and risk management. Using clustering analysis in hedging study is another innovation of this paper.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.369-384
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• 2021

In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low- and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.

• Knowledge Management Research
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• v.20 no.3
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• pp.119-137
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• 2019

With the recent spread of digital content, more people have been watching the digital content of TV programs on their PCs or mobile devices, rather than on TVs. With the change in such media use pattern, genres(types) of broadcast programs change in the flow of the times and viewers' trends. The programs that were broadcast on TVs have been released in digital content, and thereby people watching such content change their perception. For this reason, it is necessary to newly and differently classify genres(types) of broadcast programs on the basis of digital content, from the conventional classification of program genres(types) in broadcasting companies or relevant industries. Therefore, this study suggests a plan for newly classifying broadcast programs through using machine learning with the log data of people watching the programs in online media and for applying the new classification. This study is academically meaningful in the point that it analyzes and classifies program types on the basis of digital content. In addition, it is meaningful in the point that it makes use of the program classification algorithm developed in relevant industries, and especially suggests the strategy and plan for applying it.