• Animal Bioscience
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• v.35 no.3
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• pp.367-376
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• 2022

Objective: The highly pathogenic avian influenza virus (HPAIV) is a threat to the poultry industry as well as the economy and remains a potential source of pandemic infection in humans. Antiviral genes are considered a potential factor for HPAIV resistance. Therefore, in this study, we investigated gene expression related to cytokine-cytokine receptor interactions by comparing resistant and susceptible Ri chicken lines for avian influenza virus infection. Methods: Ri chickens of resistant (Mx/A; BF2/B21) and susceptible (Mx/G; BF2/B13) lines were selected by genotyping the Mx dynamin like GTPase (Mx) and major histocompatibility complex class I antigen BF2 genes. These chickens were then infected with influenza A virus subtype H5N1, and their lung tissues were collected for RNA sequencing. Results: In total, 972 differentially expressed genes (DEGs) were observed between resistant and susceptible Ri chickens, according to the gene ontology and Kyoto encyclopedia of genes and genomes pathways. In particular, DEGs associated with cytokine-cytokine receptor interactions were most abundant. The expression levels of cytokines (interleukin-1β [IL-1β], IL-6, IL-8, and IL-18), chemokines (C-C Motif chemokine ligand 4 [CCL4] and CCL17), interferons (IFN-γ), and IFN-stimulated genes (Mx1, CCL19, 2'-5'-oligoadenylate synthase-like, and protein kinase R) were higher in H5N1-resistant chickens than in H5N1-susceptible chickens. Conclusion: Resistant chickens show stronger immune responses and antiviral activity (cytokines, chemokines, and IFN-stimulated genes) than those of susceptible chickens against HPAIV infection.

• Journal of Animal Science and Technology
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• v.64 no.3
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• pp.539-563
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• 2022

Nine pig farms were evaluated for the welfare quality in Korea using animal- and environment-based parameters (particularly air quality parameters) during the winter of 2013. The Welfare Quality^® (WQ^®) protocol consists of 12 criteria within four principles. The WQ^® protocol classifies farms into four categories ranging from 'excellent' to 'not classified'. Each of these criteria has specific measures for calculating scores. Calculations for the welfare scores were conducted online using the calculation model in the WQ^® protocol. Environment-based parameters like microclimate (i.e., temperature, relative humidity, air speed, and particulate matter), bacteria (total airborne bacteria, airborne total coliform, and airborne total Escherichia coli), concentration of gases (carbon dioxide, ammonia, and hydrogen sulfide) were measured to investigate the relationship between animal- and environment-based parameters. Correlations between the results of animal- and environment-based parameters were estimated using spearman correlation coefficient. The overall assessments found that five out of nine farms were 'acceptable', and four farms were 'enhanced'; no farm was 'not classified'. The average score for the four principles across the nine farms, in decreasing order, were 'good feeding' (63.13 points) > 'good housing' (59.26 points) > 'good health' (33.47 points) > 'appropriate behaviors' (25.48 points). In the result of the environment aspect, the relative humidity of farms 2 (93.4%), 3 (100%), and 9 (98%) was much higher than the recommended maximum relative humidity of 80%, and four out of the nine farms had ammonia concentrations greater than 40 ppm. Ammonia had negative correlations with 'positive social behaviors' and positive emotional states: content, enjoying, sociable, playful, lively, happy and it had positive correlations with negative emotional states: aimless, distressed. The concentration of carbon dioxide had negative correlations with positive emotional states; calm, sociable, playful, happy and it had a positive correlation with negative emotional state; aimless. Our results indicate that the control of the environment for growing pigs can help improve their welfare, particularly via good air quality (carbon dioxide, ammonia, hydrogen sulfide).

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.69-76
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• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.

• Korean Journal of Health Education and Promotion
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• v.2 no.1
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• pp.3-50
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• 1984

The levels of health knowledge, attitude and practice of housewives considerably effect to the health of households, communities and the nation. This study was designed to grasp the levels of health knowledge, attitude and practice of houswives and analyse the various factors effecting to health in order to provide health education services as well as materials for effective formulation and implementation of health policy to improve the health of the nation. This study has been conducted through interviews by trained surveyers for 4,281 housewives selected from 4,500 households throughout the country for 40 days during July 11-August 20, 1983. The results of survey were analysed by stepwise multiple regression and path analysis are summarized as follows; 1. Based on the measurement instrument applied to this study, the levels of health knowledge, attitude and practice of housewives were extremely low with 54.5 points out of 100 points in full. Higher level with 72 points and above was approximately 21 percent and lower level with 39 points and below was approx. 24 percent. The middle level was approx. 55 percent. In order to implement health programs successively, health education should be more strengthened and to improve the level of health knowledge, attitude and practice (KAP) of the nation, political consideration as a part of spiritual reformation must be concentrated on health. 2. The level of health knowledge indicated the highest points with 57.3 the level of attitude was the second with 55.0 points and the practice level was the lowest with 50.0 point. Therefore, planning and implementation of health education program must be based on the persuasion and motivation that health knowledge turn into practice. 3. Housewives who had higher level of health knowledge, showed their practice level was relatively lower and those who had middle or low level of it practice level was the reverse. 4. Correlations among health knowledge, attitude and practice (KAP) were generally higher and statistically significant at 0.1 percent level. Correlation between total health KAP level and health knowledge was the highest with r=.8092. 5. Health KAP levels showed significant differences according to the age, number of children, marital status, self-assessed health status and concern on health of the housewives interviewed (p<0.001) 6. Health KAP levels also showed significant differences according to the education level, economic status, employment before marriage and grown-up area of the housewives interviewed. (p<0.001) 7. Heath KAP levels showed significant differences according to health insurance benificiary and the existence of patients in the family. (p<0.001). 8. Health KAP levels showed significant differences according to distance to government organizations, schools, distance to health facilities, telephone possession rate, television possession rate, newspaper reading rate and activities of Ban meeting and Women's club. (p<0.001) 9. Health KAP levels showed significant differences according to electric mass communication media such as television, radio and village broadcasting etc. and printed media such as newspaper, magazine and booklets etc., IEC variables such as individual consultation and husband-wife communication, however, there was no significance with group training. 10. Health KAP of the housewives showed close correlation with personal characteristics variables, i.e., education level (r=.5302), age (r=-.3694) grown-up area (r=.3357) and employment before marriage. In general, correlation of health knowledge level was higher than the levels of attitude or practice. In case of health concern and health insurance, correlation of practice level was higher than health knowledge level. 11. Health KAP levels showed higher correlation with community environmental characteristics, Ban meeting and activity of Women's club, however, no correlation with New-village movement. 12. Among IEC variables, husband-wife communication showed the highest correlation with health KAP levels and printed media, electric mas communication media and health consultation in order. Therefore, encouragement of husband-wife communication and development of training program for men should be included in health education program. 13. Mass media such as electric mass com. and printed media were effective for knowledge transmission and husband-wife communication and individual consultation were effective for health practice. Group training was significant for knowledge transmission, however, but not significant for attitude formation or turning to health practice. To improve health KAP levels, health knowledge should be transmitted via mass media and health consultation with health professionals and field health workers should be strengthened. 14. Correlation of health KAP levels showed that knowledge level was generally higher than that of practice and recognized that knowledge was not linked with attitude or practice. 15. The twenty-five variables effecting health KAP levels of housewives had 41 per cent explanation variances among which education level had great contribution (β=.2309) and electric mass com. media (β=.1778), husband-wife communication (β=.1482), printed media, grown-up area, and distance to government organizations in order. Variances explained (R²) of health KAP were 31%, 15%, and 30% respectively. 16. Principal variables contributed to health KAP were education level (β=.12320, β=.1465), electric mass comm. media (β=.1762, β=.1839), printed media, (β=.1383, β=.1420) husband-wife communication (β=.1004, β=.1067), grown-up area and distance to government organizations, in order. Since education level contributes greatly to health KAP of the housewives, health education including curriculum development in primary, middle and high schools must be emphasized and health science must be selected as one of the basic liberal arts subject in universities. 17. Variences explained of IEC variables to health KAP were 19% in total, 14% in knowledge, 9% in attitude, and 10% in health practice. Contributions of IEC variables to health KAP levels were printed media (β=.3882), electric mass comm media (β=.3165), husb-band wife com. (β=.2095,) and consultation on health (β=.0841) in order, however, group training showed negative effect (β=-.0402). National fund must be invested for the development of Health Program through mass media such as TV and radio etc. and for printed materials such as newspaper, magazines, phamplet etc. needed for transmission of health knowledge. 18. Variables contributed to health KAP levels through IEC variables with indirect effects were education level (Ind E=0.0410), health concern (Ind E=.0161), newspaper reading rate (Ind E=.0137), TV possession rate and activity of Ban meeting in order, however, health facility showed negative effect (Ind E=-.0232) and other variables showed direct effect but not indirect effect. 19. Among the variables effecting health KAP level, education level showed the highest in total effect (TE=.2693) then IEC (TE=.1972), grown-up city (TE=.1237), newspaper reading rate (TE=.1020), distance to government organization (TE=.095) in order. 20. Variables indicating indirect effects to health KAP levels were; at knowledge level with R²=30%, education level (Ind E=.0344), newspaper reading rate (Ind E=.0112), TV possession rate (Ind E=.0689), activity of Ban meeting (Ind E=.0079) in order and at attitude level with R²=13%, education level (Ind E=. 0338), activity of Ban meeting (Ind E=.0079), and at practice level with R²=29%. education level (Ind E=.0268), health facility (Ind E=.0830) and concern on health (Ind E=.0105). 21. Total effect to health KAP levels and IEC by variable characteristics, personal characteristics variables indicated larger than community characteristics variables. 22. Multiple Correlation Coefficient (MCC) expressed by the Personal Characteristic Variable was .5049 and explained approximately 25% of variances. MCC expressed by total Community environment variable was .4283 and explained approx. 18% of variances. MCC expressed by IEC Variables was .4380 and explained approx. 19% of variances. The most important variable effected to health KAP levels was personal characteristic and then IEC variable, Community Environment variable in order. When the IEC effected with personal characteristic or community characteristic, the MCC or the variances were relatively higher than effecting alone. Therefore it was identified that the IEC was one of the important intermediate variable.

• Journal of Embryo Transfer
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• v.27 no.3
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• pp.127-131
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• 2012

This study was performed to investigate the effects of parity and season on the embryo production in superovulated Hanwoo cows. Superovulation was performed from 1 to 8 times by repeated superovulation treatment of Hanwoo cows (n = 22). Irrespective of estrous cycle, donor cows were received a CIDR, progesterone (50 mg) and estradiol benzoate (2.5 mg). After 4.5 days, the donor cows were superovulated with total 28AU FSH (Antorin R-10) administrated twice daily in a decreasing dose for 4 days. On $6^{th}$ and $7^{th}$ of FSH injection, 2.5 mg and 15 mg $PGF_2{\alpha}$ were injected i.m, respectively. CIDR was removed at the $7^{th}$ FSH injection. The donor cows received $200{\mu}g$GnRH at 48 hrs after $1^{st}$ $PGF_2{\alpha}$ injection. The donor cows were artificially inseminated three times after estrous detection at 12 hr intervals and embryos were recovered 7 days after estrous detection. The mean number of total ova, transferrable embryos, degenerated embryos and unfertilized oocytes were $11.6{\pm}7.9$, $5.5{\pm}4.4$, $3.0{\pm}3.3$ and $2.6{\pm}4.1$ per donor cows, respectively. A higher number of total ova were recovered in parity 3~5 ($14.3{\pm}1.3$) than 1~2 ($8.9{\pm}1.9$, P<0.05). The number of recovered normal embryos is significantly higher in parity 3~5 ($7.3{\pm}0.8$) than that of over 6 ($3.7{\pm}1.5$). Significantly higher number of total ova and normal embryos were recovered in summer ($16.4{\pm}2.3$, $8.1{\pm}1.4$) than in autumn ($10.1{\pm}1.8$, $4.5{\pm}1.1$) and winter ($6.3{\pm}1.8$, $3.3{\pm}1.1$), respectively (P<0.05). Transferable embryos were significantly higher in summer ($7.6{\pm}1.3$) than in winter ($3.0{\pm}1.0$, P< 0.05). The results were showed that parity and season affecting on the production of embryos in superovulated Hanwoo.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.