• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.135-147
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• 2003

This study used the multistage hierarchical clustering image classification to analyze the satellite images for the land-cover types of an area in the Korean peninsula. The multistage algorithm consists of two stages. The first stage performs region-growing segmentation by employing a hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous, and finally the whole image space is segmented into sub-regions where adjacent regions have different physical properties. Without spatial constraints for merging, the second stage clusters the segments resulting from the previous stage. The image classification of hierarchical clustering, which merges step-by step two small groups into one large one based on the hierarchical structure of digital imagery, generates a hierarchical tree of the relation between the classified regions. The experimental results show that the hierarchical tree has the detailed information on the hierarchical structure of land-use and more detailed spectral information is required for the correct analysis of land-cover types.

• Environmental Engineering Research
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• v.19 no.3
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• pp.197-203
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• 2014

This study estimated spatial and seasonal variation of water quality to understand characteristics of Nakdong river basin, Korea. All together 11 parameters (discharge, water temperature, dissolved oxygen, 5-day biochemical oxygen demand, chemical oxygen demand, pH, suspended solids, electrical conductivity, total nitrogen, total phosphorus, and total organic carbon) at 22 different sites for the period of 2003-2011 were analyzed using multivariate statistical techniques (cluster analysis, principal component analysis and factor analysis). Hierarchical cluster analysis grouped whole river basin into three zones, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) based on similarity of water quality characteristics. The results of factor analysis/principal component analysis explained up to 83.0%, 81.7% and 82.7% of total variance in water quality data of LP, MP, and HP zones, respectively. The rotated components of PCA obtained from factor analysis indicate that the parameters responsible for water quality variations were mainly related to discharge and total pollution loads (non-point pollution source) in LP, MP and HP areas; organic and nutrient pollution in LP and HP zones; and temperature, DO and TN in LP zone. This study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of multi-parameter, multi-location and multi-year data sets.

• Journal of Animal Science and Technology
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• v.54 no.6
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• pp.463-469
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• 2012

This study utilized a standardization and cluster analysis technique for the selection and classification of beneficial bacteria. A set of synthetic data consisting of 100 individual variables with three characteristics was created for analysis. The three characteristics assigned to each independent variable were designated to have different numeric scales, averages, and standard deviations. The variables were bacterial isolates at random, and the three characteristics were fermentation products, including cell yield, antioxidant activity of culture, and enzyme production. A standardization method utilizing a standard normal distribution equation to record fermentation yields of each isolate was employed to weight their different numeric scales and deviations. Following transformation, the data set was analyzed by cluster analysis. The Manhattan method for dissimilarity matrix construction along with complete linkage technique, an agglomerative method for hierarchical cluster analysis, was employed using statistical computing program R. A total of 100 isolates were classified into groups A, B, and C. In a comparison of the characteristics of each group, all characteristics in groups A and C were higher than those of group B. Isolates displaying higher cell yield were classified as group A, whereas those isolates showing high antioxidant activity and enzyme production were assigned to group C. The results of the cluster analysis can be useful for the classification of numerous isolates and the preparation of an isolation pool using numerical or statistical tools. The present study suggests that a simple technique can be applied to screen and select beneficial microbes using the freely downloadable statistical computing program R.

• Journal of the Korean Dietetic Association
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• v.19 no.1
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• pp.82-94
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• 2013

The purposes of this study were to measure the service mind and customer orientation of employees and to identify the effect of service mind on customer orientation in elementary school foodservices. The questionnaires were distributed to foodservice employees of the 19 elementary schools, but collected from 12 schools in Gwangju, Gyeonggi. The statistical data analysis was completed using SPSS (ver. 18.0) for the independent sample t-test, ANOVA, Cronbach's alpha, principal component analysis, hierarchical & K-means cluster analysis, Pearson' correlation analysis, and multiple regression analysis. Foodservice employees highly rated their service mind (3.94 out of 5 points), especially their perceptions on the importance of service (4.13 points). The effort to provide service was significantly different depending on the serving place (P<0.05). Employees had a high level of customer orientation (4.02 points), which was significantly influenced by age, position, or career (P<0.05), and cook license (P<0.01). As a result of cluster analysis for service mind, employees were divided into two groups: a low-service mind group (cluster 1) and a high-service mind group (cluster 2). Cluster 2 had a significantly higher overall customer orientation than cluster 1 (P<0.001). The pride in providing services (${\beta}$=0.390, P<0.01) and the perception of the importance of services (${\beta}$=0.297, P<0.05) showed a significant and positive effect on customer orientation.

• Journal of the Mineralogical Society of Korea
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• v.12 no.2
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• pp.77-90
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• 1999

Characteristics and variations of chemical compositions in Co-rich crusts occurred in the EEZ of the Republic of Marshall Islands were reviewed. Correlation coefficient analysis, hierarchical cluster analysis, and Q-mode factor analysis for 62 samples were done in this study. All data were selected and gathered from the open file report of the cooperative cruise done by United States Geological Survey with Scripps Institute of Oceanography, University of Hawaii or Korea Ocean Research Development Institute. The average of crust thickness. Co content, and Ni content of 62 samples from the 21 seamounts were 30mm, 0.58 wt% and 0.40%, respectively. The mineral phases and associated elements assigned by correlation coefficients, cluster analysis and Q-mode factor analysis are following four. 1) CFA: P, Ca, CO2, Y, Sr: 2) Mn-oxide mineral: As, Mn, Co, Na: 3) Al-silicate mineral: Pd,Si, Al, Cu, Fe: 4) PGE-bearing mineral: Rh, Pt, Ir.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.41-47
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• 2015

In recent years, energy supply cases to take advantage of EMS(Energy Management System) are increasing according to high interest of energy efficiency. The important factor for essential and economical EMS operation is the supply and demand plan the hourly power demand of building load using the hierarchical clustering method of variety statistical techniques, and use the real historical data of target load. Also the estimated results of study are obtained the reliability through separate tests of validity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3468-3495
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• 2015

In this paper, we present a multi-chain based hierarchical topology control algorithm (MCHTC) for wireless sensor networks. In this algorithm, the topology control process using static clustering is divided into sensing layer that is composed by sensor nodes and multi-hop data forwarding layer that is composed by leader nodes. The communication cost and residual energy of nodes are considered to organize nodes into a chain in each cluster, and leader nodes form a tree topology. Leader nodes are elected based on the residual energy and distance between themselves and the base station. Analysis and simulation results show that MCHTC outperforms LEACH, PEGASIS and IEEPB in terms of network lifetime, energy consumption and network energy balance.

• 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.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.265-276
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• 2015

In this paper, we propose hierarchical cluster analysis and multidimensional scaling for joint distribution valued data. Information technology is increasing the necessity of statistical methods for large and complex data. Symbolic Data Analysis (SDA) is an attractive framework for the data. In SDA, target objects are typically represented by aggregated data. Most methods on SDA deal with objects represented as intervals and histograms. However, those methods cannot consider information among variables including correlation. In addition, objects represented as a joint distribution can contain information among variables. Therefore, we focus on methods for joint distribution valued data. We expanded the two well-known exploratory methods using the dissimilarities adopted Hall Type relative projection index among joint distribution valued data. We show a simulation study and an actual example of proposed methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.63-70
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• 2015

The numerous improved schemes of user authentication based on password have been proposed in order to prevent the data access from the unauthorized person. The importance of user authentication has been remarkably growing in the expanding application areas of wireless sensor networks. Recently, emerging wireless sensor networks possesses a hierarchy among the nodes which are divided into cluster heads and sensor nodes. Such hierarchical wireless sensor networks have more operational advantages by reducing the energy consumption and traffic load. In 2012, Das et al. proposed a user authentication scheme to be applicable for the hierarchical wireless sensor networks. Das et al. claimed that their scheme is effectively secure against the various security flaws. In this paper, author will prove that Das et al.'s scheme is still vulnerable to man-in-the-middle attack, password guessing/change attack and does not support mutual authentication between the user and the cluster heads.