• Information and Communications Magazine
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• v.33 no.10
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• pp.60-64
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• 2016

본고에서는 베이지안 기계학습 방법론에 대해서 간략히 살펴본다. 특히, 복잡한 자료들 사이의 관계를 규명하는 것이 목적이며 비지도학습(unsupervised learning)의 한 분야인 군집분석에서 베이지안 방법론들이 어떻게 사용되어지는지를 설명한다. 군집의 수를 사전에 아는 경우에 사용되는 모수적 베이지안 방법을 간단하게 설명하고, 군집의 수까지 추론 할 수 있는 비모수 베이지안방법에 대해서 자세하게 다룬다.

• Korean Journal of Ecology and Environment
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• v.52 no.3
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• pp.274-283
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• 2019

Beta diversity is an efficient means of assessing the spatial variation in community composition among sites. To present fish community variation and LCBD (Local Contribution to Beta Diversity) among sites in stream, 6 sampling sites were selected in Cheonggye stream. Fish communities, environmental and habitat variables were collected at sites from April 2014 to October 2015. We used the total variance of the fish community data table (site-by-species community table) based on different forms, presence-absence, abundance, and Hellinger transformation, to estimate and compare beta diversity and LCBD. Fish community data table transformed by Hellinger distance showed the higher values of beta diversity than presence-absence and abundance data table. A similar patterns of LCBD were observed with presence-absence and Hellinger transformed data table. Low value of beta diversity calculated by community data table with abundance was due to the non-normality of fish assemblage data. Additionally, correlation coefficients were calculated to evaluate the relationships among LCBD, community indices and physicochemical variables. LCBD showed negative correlation coefficients with Shannon diversity. Overall, application of beta diversity analysis is an efficient method of addressing spatial variation of fish communities and ecological uniqueness of the sites in stream.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.969-977
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• 2021

We cluster the electricity consumption of households in A-apartment in Seoul, Korea using Hierarchical K-means clustering algorithm. The data is recorded from the advanced metering infrastructure (AMI), and we focus on the electricity consumption during evening weekdays in summer. Compare to the conventional clustering algorithms, Hierarchical K-means clustering algorithm is recently applied to the electricity usage data, and it can identify usage patterns while reducing dimension. We apply Hierarchical K-means algorithm to the AMI data, and compare the results based on the various clustering validity indexes. The results show that the electricity usage patterns are well-identified, and it is expected to be utilized as a major basis for future applications in various fields.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.557-565
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• 2021

This study was attempted to identify differences in self-esteem and grit in adolescents depending on the type of parenting attitude. Among the Korea Children Youth Panel Survey conducted by National Youth Policy Institute, the data of 2,438 first-year middle school students in 2018 year were analyzed. The collected data were analyzed using hierarchical cluster analysis and k-mean cluster analysis. As a result, the adolescent's perceived parenting attitude was classified into four types: 'passive affection acceptance', 'active affection acceptance', 'authoritarian inconsistency', and 'lack of affection rejection'. Also, there were significant differences in self-esteem and the degree of grit among the four clusters of parenting attitudes. Both self-esteem and grit were highest in the "active affection acceptance" group 2. In the future, differentiated parental education is needed for each cluster to improve self-esteem and grit of adolescents, and this study can be used as a basic data for the development of educational programs.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.139-144
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• 2011

This study proposed a classification method of LIDAR data by using simultaneously the color information (R, G, B) and reflection intensity information (I) obtained from terrestrial LIDAR and by analyzing the association between these data through the use of statistical classification methods. To this end, first, the factors that maximize variance were calculated using the variables, R, G, B, and I, whereby the factor matrix between the principal factor and each variable was calculated. However, although the factor matrix shows basic data by reducing them, it is difficult to know clearly which variables become highly associated by which factors; therefore, Varimax method from orthogonal rotation was used to obtain the factor matrix and then the factor scores were calculated. And, by using a non-hierarchical clustering method, K-mean method, a cluster analysis was performed on the factor scores obtained via K-mean method as factor analysis, and afterwards the classification accuracy of the terrestrial LiDAR data was evaluated.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.677-686
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• 2009

Stratification of population is widely used to improve the efficiency of the estimation in a sample survey. However, it causes several problems when there are some variables containing outliers. To overcome these problems, Park and Yun (2008) proposed a rather subjective method, which finds outliers before $\kappa$-means clustering for stratification. In this study, we propose the $\kappa$-spatial medians clustering method which is more robust than $\kappa$-means clustering method and also does not need the process of finding outliers in advance. We investigate the characteristics of the proposed method through a case study used in Park and Yun (2008) and confirm the efficiency of the proposed method.

• 한국노년학
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• v.32 no.3
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• pp.675-687
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• 2012

This study is aimed to investigate the difference of leisure time use of elderly groups clustered with demographic factors and finally produce the leisure profile. A total 5392 time diaries, filled up by 65-years old and over in 2009 Time Use Survey, were analyzed by using descriptive statistics, correlation, cluster analysis, and ANOVA accompanied with the Scheffe's tests. The subjects could be clustered into 7 groups such as still working, work than leisure, hobby with friends, various leisure in a city, with religion and friendship, working and doing leisure in their spare time, and leisure in suburb based upon demographic factors such as age, gender, spouse, education, job, income and living area. The data also revealed that the clusters had different time use patterns in pursuing leisure. Overall, leisure in later life could be influenced by working status, and play an important role in filling their loss of job or spouse. The findings could help us plan upcoming leisure program and policies for the elderly.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.3 no.2
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• pp.71-79
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• 1998

Fish larvae and environmental variables were collected in the sea around Cheju Island of Korea in November, 1986. Fish larvae were found at the 43 of 44 stations. Abundance of fish larvae rangεd from 3 to 259 indiv./$10^3\;m^3$ (average 50 indiv./$10^3\;m^3$) Dominant species were Engraulis japonicus, Champsodon snyderi, Gobiidae, Sebastiscus marmoratus, and Synagrops philippinensis, which comprised 56.6% of total fish larvae abundance. 43 stations were c1assified into two groups by c1uster analysis based on the abundance of fish taxa. Most stations of Group I were located in the middle reach of Tsushima Current and had higher number of species and abundance than the sites of Group II which cover the marginal areas of Tsushima Current, Korean Coastal Waters, and coastal water of China. The dominant taxa in the sites of the Group I were E. japonicus, Gobiidae, S. philippinensis, Aulops japonicus, Bregmaceros spp., Benthosema pterotum, Lampanctus spp., etc., all of which are typical warm-water species. Therefore, Tsushima Current seems to be the most critical factor regulating the structure of fish larvae community in the study area. The discriminant analysis using the environmenta1 variables (temperature, salinity, and dissolved oxygen) produced result with a difference only of 11.6% from that of c1uster analysis using the taxa composition and abundance data. Consequently, the structural type of fish larvae community can be used for predicting the changes of environmental conditions in the study area.

• Korean Journal of Environment and Ecology
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• v.9 no.2
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• pp.166-181
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• 1996

This study was conducted to investigate the community structure of old-growth forest and vegetation-environment relationships in the Sangwonsa-Bironbong area, Odaesan National Park. TWINSPAN and DCA were used to identify and describe the community types for 45 sample sites. The results of DCA ordination showed that soil pH, soil organic matters and soil moisture played a role in determining community types in the study area. Community types of the study area were role in determining community types in the study area. Community types of the study area were classified into 6 groups by TWINSPAN; Abies holophylla-Carpinus koraiensis community. Each community types were significantly different in soil pH and soil organic matters. And each community types can be explained by geographical features.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.723-733
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• 2009

The crucial problems of K-means clustering are deciding the number of clusters and initial centroids of clusters. Hence, the steps of K-means clustering are generally consisted of two-stage clustering procedure. The first stage is to run hierarchical clusters to obtain the number of clusters and cluster centroids and second stage is to run nonhierarchical K-means clustering using the results of first stage. Here we provide automated K-means clustering procedure to be useful to obtain initial centroids of clusters which can also be useful for large data sets, and provide software program implemented using R.