Earticle

Big data is the compilation of enormous data that arrives from diverse sources for instance online transaction details, social media, sensor data, etc. Such assortment of enormous data develop into tough to evaluate by conventional processing relevance’s. By the development and upcoming latent in the healthcare business field, it is essential to analyze a enormous noisy data to get significant information. In healthcare system, the aim of this work is to evaluate the medical database of diabetes patients by a mixture of innovative hierarchical decision attention network, association rules (AR) and multiclass outlier classification with MapReduce framework. The association rule apriori algorithm in a MapReduce framework considers health data to create regulations. This is employed to discover the association among disease and their signs. This examination is made by means of UCI machine learning datasets of diabetes containing 50 attributes. The results of the proposed algorithm are offered by parameters for instance precision, accuracy, recall, and F-score.

In visual robot place recognition (VPR), a scene graph is a rich scene model that can describe the complex contexts in a scene such as the relationships between various types of visual contents including appearance, space, and semantics. However, training an efficient scene graph classifier is not straightforward. Existing approaches typically rely on exhaustive matching between query and database graphs and are not scalable to large-size VPR problems. Our research is motivated by a recent development of the graph convolutional neural network (GCN) as an efficient and discriminative classifier for graph data, and it aims to explore the potential of the GCN as a scene graph classifier. However, unlike several existing GCN applications, no valid scene graph descriptor for a GCN classifier on noisy scene data exists. To address this issue, herein, we propose to train the GCN model in a teacher-to-student knowledge transfer scheme by employing an existing state-of-the-art single-view VPR system as the teacher model. The proposed approach is implemented within a practical VPR framework by combining the best of the following three independent fields: multimodal information retrieval, rank matching, and similarity-based pattern recognition. Experiments using the public NCLT dataset validate the effectiveness of the proposed approach.

The popularity of the Internet introduces many network-enabled services that can be accessed by the user. But the adversaries are trying to deny these critical services to the user through Denial of Service (DoS) attacks. Presently, dealing with DoS attack which targets the application layer using slow traffic rate is one of the key challenges faced by the service providers. In this paper, a deep classification model using flow data is proposed to detect slow DoS attack on HTTP. The classifier is evaluated using CICIDS2017 dataset. The results obtained show that the classifier can obtain 99.61% accuracy.

PurposeThis study aimed to classify self-care patterns among Korean adults with prediabetes using an unsupervised machine learning approach. The classification was grounded in Orem’s Self-Care Theory, focusing on self-care demands, self-care agencies, and self-care behaviors. MethodsA secondary data analysis was conducted using the 2023 Korea National Health and Nutrition Examination Survey. Variables were selected and categorized according to the theoretical components of Orem’s model. Principal component analysis was applied for dimensionality reduction, followed by K-means clustering to identify distinct self-care pattern groups. All variables were standardized using min-max normalization. Group differences were examined using analysis of variance and the chi-square test. ResultsThree self-care pattern groups were identified: the high self-care performance group, the latent self-care risk group, and the self-care vulnerable group. These groups exhibited distinct profiles across self-care demands, agencies, and behaviors. Significant intergroup differences were also observed in education level, income, health literacy, fasting blood glucose, and hemoglobin A1c levels. ConclusionSelf-care patterns among adults with prediabetes can be effectively classified through unsupervised learning techniques. The findings highlight the importance of developing tailored nursing interventions that consider multidimensional self-care profiles. This study underscores the applicability of Orem’s Self-Care Theory and demonstrates the potential of machine learning in identifying at-risk subgroups for early intervention.

Background Multiple factors are associated with lower back and lower limb (LB & LL) pain, such as impaired muscle strength, balance, endurance, and motor control, and altered movement patterns. Symmetry of motion, strength and balance are goals for rehabilitation in patients with LB & LL pain. When classifying patients before or during on- and offline assessment, it is necessary that an easy to use functional test be available for clinicians. Purpose To establish a classification tree model for discriminating people with and without LB & LL pain during walking using symmetry values from side plank endurance test, hip abductor strength test, one-leg standing time tests and walking tests. Study design Cross-sectional study Methods A total of 100 subjects with and without LB & LL pain during walking participated. We measured the side plank endurance time, hip abductor strength and one-leg standing time with eyes open and closed, and the sagittal and frontal head angles at comfortable and fast walking speeds using a wearable wireless earbud sensor and calculated the symmetry index (SI) for each test. Classification and regression tree analysis with 10-fold cross validation was used to develop the classification model. Results The classification tree had 83% accuracy for discriminating people with and without LB & LL pain during walking. The most important factor for classification was the SI of the one-leg standing time with eyes closed; the second-most important factor was the SI of the frontal head angle during fast walking. Conclusions The present classification model can differentiate people with and without LB & LL pain during walking based on symmetry data acquired during functional tests, such as one-leg standing time with the eyes closed and fast walking test using the wearable device. Based on the present results, clinicians can classify patients before and during on- and offline assessments using cutoff values of the SI of the one-leg standing test with eyes closed of 63.88%, and of frontal head motion during a fast-walking test of 63.31%.

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

Text classification is a challenging task, especially when dealing with a huge amount of text data. The performance of a classification model can be varied depending on what type of words contained in the document corpus and what type of features generated for classification. Aside from proposing a new modified version of the existing algorithm or creating a new algorithm, we attempt to modify the use of data. The classifier performance is usually affected by the quality of learning data as the classifier is built based on these training data. We assume that the data from different domains might have different characteristics of noise, which can be utilized in the process of learning the classifier. Therefore, we attempt to enhance the robustness of the classifier by injecting the heterogeneous data artificially into the learning process in order to improve the classification accuracy. Semi-supervised approach was applied for utilizing the heterogeneous data in the process of learning the document classifier. However, the performance of document classifier might be degraded by the unlabeled data. Therefore, we further proposed an algorithm to extract only the documents that contribute to the accuracy improvement of the classifier.

Predicting disaster classification on time is critical to mitigate the damage. Since identifying disaster types requires large amounts of data, and real-world data are often imbalanced, there are many recent works addressing data imbalance problems using generative models. However, if the process of generating text data based on disaster classes and severity is not handled improperly, the quality of the data can be degraded as well as the performance of classification predictions. In this paper, we propose a scheme for generating data with enhanced quality using text based on labels such as informational value of text and severity of disasters. Our experiment results verify the quality of data through the comparisons of prediction performance between various machine learning models.

확장된 데이터 표현의 주요 목표는 유비쿼터스 환경에서 일반적인 문제에 적합한 데이터 구조를 개발하는 것이다. 이 방법의 가장 큰 특징은 속성 값을 확률로 표현할 수 있다는 것이다. 다음 특성은 훈련 데이터의 각 이벤트가 중요도를 나타내는 가중치 값을 갖도록 한다는 것이다. 데이터 구조가 개발된 후에 이를 학습할 수 있는 알고리즘이 고안된다. 그 동안 이 알고리즘은 여러 분야에서 여러 문제에 적용하여 좋은 결과를 산출해 왔다. 본 논문은 먼저 데이터 표현 기법인 UChoo를 소개하고 이론적인 배경이 되는 규칙 개선 문제를 소개한다. 그리고 규칙 개선, 손실 데이터 처리, BEWS 문제, 앙상블 시스템과 같은 응용 분야의 예를 소개한다.

The main goal of extended data expression is to develop a data structure suitable for common problems in ubiquitous environments. The greatest feature of this method is that the attribute values can be represented with probability. The next feature is that each event in the training data has a weight value that represents its importance. After this data structure has been developed, an algorithm has been devised that can learn it. In the meantime, this algorithm has been applied to various problems in various fields to obtain good results. This paper first introduces the extended data expression technique, UChoo, and rule refinement method, which are the theoretical basis. Next, this paper introduces some examples of application areas such as rule refinement, missing data processing, BEWS problem, and ensemble system.

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

This bibliometric study is a citation analysis of journals that concern themselves with businessrelated topics. For this purpose, the journals from the following five categories from the Web of Science Subject Categories have been examined: “Business”, “Business & Finance”, “Economics”, “Management” and “Operations Research & Management Science”. The data is retrieved from the Journal Citation Reports 2019 for each journal that is part of one of the categories. The data includes information about the journals that the articles published in the specific journal in 2019 cited and which journals cited the articles in a specific journal (Cited Journal Data and Citing Journal Data). This data is combined by creating an asymmetrical 1- mode matrix of all journals. Then, the matrix is analyzed with Pajek and VOSviewer to create clusters of journals with a high inter-correlation through citations. In the following step, the journals in these clusters are analyzed for their disciplines, main topics, and compared to typical fields of study at universities (e.g. Accounting, Marketing, Finance, etc.) in order to highlight similarities and differences between the fields of studies and the clusters of journals.

비만은 세계보건기구에서 질병으로 규정하고 있으며, 신체 내외부적인 영향을 나타낸다. 본 연구는 머신러닝을 이용 하여 생활패턴에 따른 비만도를 예측하고자 한다. 머신러닝에 이용한 데이터는 오픈데이터를 사용하였으며, 머신러닝 모델은 구글 코랩, 파이썬을 이용하고 모델 구성은 Light Gradient Boosting Machine, Extreme Gradient Boosting, Decision Tree Classifier, K Neighbors Classifier, Naive bayes 총 5개의 모델로 구성하였다. 각 모델 성능 평가 지표는 정확도, area under curve, 재현율, 정밀도, F1-score로 평가하였다. 해당 데이터를 활용한 5개 모델 중 Light Gradient Boosting Machine이 모든 지표에서 성능이 가장 우수 했으며, 지표에 대한 결과는 정확도 0.9601, area under curve 0.9981, 재현율 0.9601, 정밀도 0.9611, F1 score 0.9601이었다. 본 연구를 통해 기본적인 생활 패턴에 따른 비만도 예측을 통해 비만에 대한 사전 예방이 가능할 것으로 사료된다.

Obesity is defined as a disease by the World Health Organization and indicates internal and external influences on the body. This study aims to predict obesity according to lifestyle patterns using machine learning. The data used for machine learning used open data, and the machine learning model used Google Colab and Python. The model configuration consisted of a total of five models: Light Gradient Boosting Machine, Extreme Gradient Boosting, Decision Tree Classifier, K Neighbors Classifier, and Naive Bayes. The performance evaluation indices for each model were accuracy, area under curve, recall, precision, and F1-score. Among the five models using the data, Light Gradient Boosting Machine showed the best performance in all indices, and the results for the indices were accuracy 0.9601, area under curve 0.9981, recall 0.9601, precision 0.9611, and F1-score 0.9601. Through this study, it is believed that it will be possible to prevent obesity in advance by predicting obesity according to basic lifestyle patterns.

본 연구는 Jenks Natural Breaks 기법을 사용하여 자연재난에 대한 전국 격자기반의 잠재적 재해위험도 평가체계를 구축한다. 각 대상체별 잠재적 위험 요소인 노출지표 및 취약지표를 구성하여 총 9개의 대상체인 인구, 공업 시설, 공용 시설, 교육 연구 시설, 의료 복지 시설, 편의 시설, 농업, 축산업, 도로에 대해 평가한다. 모든 지표는 전국 대상의 국가지점번호 격자단위인 107,555개의 데이터로 구축하였고, 각 대상체별로 Jenks Natural Breaks 기법을 적용하여 Level 1부터 Level 4까지의 총 4단계의 잠재적 재해위험도 평가 기준을 산정하였다. 또한 도출된 평가 지표 및 평가 기준을 활용하여 전국의 9개의 대상체에 대한 잠재적 재해위험도를 평가하였다. 이를 바탕으로 우리나라에서 발생하는 자연재해 위험 영향 평가를 수행할 수 있는 체계를 마련하였으며, 향후 격자기반의 잠재재난위험영향 산정 등과 같은 추가적인 연구를 진행할 수 있는 가능성을 발견하였다.

This paper proposes a disaster risk assessment system for the natural disaster with Jenks Natural Breaks Classification. The exposure indicators and vulnerability indicators are organized to disaster risk factors for the each receptors. The receptors evaluated the risk assessment are 9 and composed of people, industry, public facilities, educational and research facilities, medical and welfare facilities, amenity facilities, agriculture, livestock industry, and roads. All indicators are composed of 107,555 grid-based data having the codes of the region in the country. With Jenks natural breaks classification, the disaster risk assessment criteria for each receptors were presented per the disaster risk grade (Level 1 ~ Level 4). All of the criteria were evaluated with the grid-base data per the receptors and the evaluated results were presented by the maps of Korea. Through this study, the disaster risk assessment criteria can be used as the reference and forecasting for the natural disaster in Korea.

Travel trends are changing due to the prolonged COVID-19 pandemic and vaccine development. The analysis of pre and post Covid-19 tourism trends, according to a survey by Jeju Tourism Organization, shows that the search volume for overseas travel has decreased compared to 2018 and 2019, but search volume for Jeju travel and the number of tourists visiting Jeju Island Increased. In the case of tourists in Jeju, many use vehicles, mostly rental cars, for transportation due to the geographical characteristics of the island, and the number of electric vehicles is increasing in Jeju Island’s rental car services due to the strengthening of electric car policies. However, most of the existing research on tourists have been conducted using public data. Therefore, based on the means of transportation mainly used by tourists, electric vehicle driving data recorded for three years provided by Korea Electric Power Corporation Knowledge Data Network (KEPCO KDN) was classified into a total of 11 areas by weather and time requirements and classified through an artificial intelligence-based multiclassification model. In this study, tourist activity patterns were classified according to season, time zone, and climate conditions, but in the future, it can be used for recommendations and advertisements for tourist destinations by subdividing zones and adding information on users.

This study aims to identify an AI classification model that is optimal for the classification of linguistic data. For this purpose, three commonly used classification models (XGBoost classifier, Random Forest Classifier, and SVM classifier) are compared in terms of their performance. Specifically, the three models are trained to classify the input data into essays and dialogues based on the syntactic complexity-related characteristics that distinguish between essays and dialogues. To determine if a model performing well on balanced data also performs well on imbalanced data, the three models’ performances are measured under two conditions: when the training dataset is balanced and when it is imbalanced. The performances of the trained models on the first test dataset are evaluated using accuracy, F1-score, normalized confusion matrix, and the area under the receiver operating characteristic curve. The performances on the second test dataset are assessed in terms of accuracy, confusion matrix, precision, and recall. The results demonstrate that the Random Forest Classifier has the best performance among the three models regardless of the balance of training data.

High-resolution land cover maps are essential in fields such as forest resource management, urban green space planning, and environmental protection. In recent years, Unmanned Aerial Vehicles (UAVs) have increasingly become influential in land cover mapping due to their flexibility, low cost, and fast data acquisition capability. However, accurately classifying high-resolution image data collected by UAVs remains a challenge due to the complexity of the data and the substantial computational resources required for processing. To address this problem, this study combines UAV remote sensing data with Object-Based Image Analysis (OBIA) to optimize feature selection to improve the accuracy of land cover classification and provide more reliable data support. In this study, combinations of four feature types were evaluated using a Decision Tree (DT) algorithm in eight scenarios. The results showed that a comparison with spectral features alone and the combination of other feature types can significantly improve the classification accuracy. Height features contribute the most to enhancing the classification results, followed by spectral and geometric features, while the contribution of texture features is relatively limited. In addition, the optimal feature combination selected by the Recursive Feature Elimination (RFE) method further validates its effectiveness in improving land cover classification results. Finally, the best feature combination achieved a classification accuracy of 72.00% and a Kappa coefficient of 0.6543, proving the effectiveness of the feature selection and optimization strategy.

정보화 투자의 증가는 정보기술자원의 복잡성을 증대시켰으며, 이로 인해 중복투자 및 정보시 스템 관리의 비효율성이 대두 되었다. 이에 정보기술 자원에 대한 효율적 도입과 관리를 위하여 EA(Enterprise Architecture) 사상이 발전하였다. 하지만, 데이터 영역에 대해서는 EA 사상이 발전 한 이후에도 한동안 체계적인 지침의 제공이 미흡하였다. 따라서, 이제는 프로세스에 숨어있는 데이터 를 도출, 관리하여 공공부문 각 기관 간 데이터의 공유성 및 재활용성을 높이기 위한 데이터 참조모 형(DRM : Data Reference Model)의 활용이 필요하다. 이에 본 연구에서는 데이터 참조모형 중 가 장 활용도가 높은 데이터 분류체계에 대한 개선의 필요성을 인식하고, 분류체계 재정립을 위한 방향 성 및 연구 방법을 제시해 보려 한다.

The investment on informatization has caused the increase in complexity of IT resources as well as inefficiency of Information system management and redundant investment. Thus, the idea of EA (Enterprise Architecture) was developed in order to manage the IT resources efficiently but the systematical guideline was insufficiently provided in data area.. Because of this, it is necessary to make full use of DRM (Data Reference Model) so we can enhance the sharing and reuse of data between several institutions in public sector by extracting and managing data behind processes. In this study, we recognize the necessity of improvement on data classification which is highly utilized and present the direction for the data classification rethesis.

빅데이터 기술의 발달로 4차 산업혁명이 시작되면서 스마트 팩토리에 대한 관심이 증가하고 있 다. 제조업에서는 여러 종류의 데이터들이 기하급수적으로 증가하고 있지만 관리하기가 어렵고, 데이 터가 수집되어도 중요한 데이터를 찾기 힘들뿐더러 어떠한 데이터를 어떻게 사용하여야 할지도 알 수 없다. 또한, 기존의 공정에서는 공정물품에 대해 양품과 불량품만을 구분하여 불량품에 대해 작업자가 직접 눈으로 가성불량품과 불량품을 구별하였다. 이 경우 시간이 오래 걸릴뿐더러 작업자의 상태에 따 라 생산성이 낮아지는 현상이 발생한다. 본 논문에서는 이러한 문제점을 해결하기 위해 딥러닝을 이 용한 제조 공정 영상을 분류하는 기법을 제안한다. 제안하는 방법은 CNN(Convolutional Nueral Network)를 이용하여 화상검사 공정에서 결과로 나오는 2588*1940 크기의 영상에 대해 양품, 불량 품, 가성불량(조명, 퓨즈, 뒤틀림(왜곡))에 대해 학습시켜 분류하고 테스트한다. 그 결과로 양품과 진성 불량품에 대해 98% 정확도를 확인하였고, 가성불량에 대해서는 93%의 정확도를 확인할 수 있었다.

Interest in smart factory is increasing with the growth of 4th industrial revolution due to the development of big data technology. Diverse kinds of data in the manufacturing industry are growing exponentially, but it is difficult to manage, and even if the data is collected, it is hard to find important data and find the appropriate way to use the data. In addition, the worker sorted out defective products with the pseudo-defective products only through his/her direct eyes in the original manufacturing process. This takes long time and also is influenced a lot by the individual workers’ capability. In this paper, we propose a manufacturing process image classification method using deep learning to solve these problems. The proposed method uses CNN(Convolutional Neural Network) to learn the well-made, defective, and pseudo-defective (lights, fuse, distortion) products with the 2588*1940 size image that comes out as a result in the image inspection process. The outcomes show 98% accuracy in well-made and defective products, and 93% accuracy in pseudodefective products.

본고는 국어사 정보를 전망적으로 서술하는 확장형 국어대사전을 감안하여 국어사전에 국어사 정보를 반영하는 방안을 검토하였다. 통시 국어(대)사전은 시기별로 이루어진 공시 국어사전을 바탕으로 수립될 것인바, 이때 고려될 방안을 미시구조에 반영될 국어사 정보별로 서술하였다. 본고에서는 먼저, 문헌 자료에 드러난 국어사 정보의 성격이 국어사전에 실리는 국어 정보와 어떻게 다른 속성을 보이는지 확인하고, 문헌자료에서 확인되는 변이와 변형을 국어사전에 반영하는 방안을 점검하였다. 문헌자료에는 의소에 대당하는 형태가 확인되는바, 국어사전에서는 이를 형태 단위별로 재정리하여 사전 이용자에게 필요한 서술 단위에 맞추어 국어사 정보를 제공하게 된다. 본 연구에서는 통시 국어대사전에서도 현대국어 확장형 국어사전에서처럼 전면적인 서술이 이루어질 필요가 있음을 전제하였다. 또한 사전 이용 매체의 변화 추세에 따라 통시 국어사전도 온라인 전자사전을 기반으로 하여야 하며 따라서 국어사 정보의 정리와 검색의 편의를 위해서 검색어 정보를 별도로 마련할 것을 제안하였다.

This study is a methodological survey for data classification and representation in the Korean historical lexicography. For the survey, I readjust the microstructure of historical dictionary. In doing so, I suggest Korean language data must be classified for the user-oriented historical lexicography. For the ease of retrieving historical information of Korean language, Korean language dictionary making must be based on user orientation. It is representation of classified details of Korean language data that makes the dictionary successful. So, the historical lexicography starts from searching presentational methodology.

본 연구는 소득구간별 아동ㆍ청소년의 사회관계에 의한 건강행위 분류예측에 관한 변수 중요도를 분석하기 위해 데이터마이닝 기법을 활용하였다. jamovi 및 R(ver.4.1.0)/R-studio 분석도구로 활용하여 한국아동ㆍ청소년패널조사2018 패널자료의 2차 년도(2019년) 데이터 자료(아동ㆍ청소년 총5,197명과 그들의 보호자 5,197명)를 분석하였고 연구결과는 다음과 같다. 첫째, 혼합분포군집분석을 통해 아동ㆍ청소년의 건강행위 특성에 의해 불건강군집(1군집)과 건강군집(2군집)으로 분류되었으며, 건강군집이 불건강 군집에 비해 주관적 건강인식과 신체활동정도, 이침 식사 정도가 높게 나타났다. 둘째, t 검정 결과 교사관계, 부모양육태도, 부모와 함께하는 시간, 어머니 학력, 부모의 아침식사여부, 가구소득에서 유의미한 차이가 나타났다. 셋째, 소득구간별 아동ㆍ청소년의 건강행위 특성에 영향을 미치는 사회관계요소가 소득상위구간에 유리하게 작용됨을 확인할 수 있었다. 그리고 사회관계로써 친구관계, 교사관계, 부모관계 그리고 부모요인(학력, 신체활동, 아침식사여부) 중에서 부모의 아침식사여부가 모든 소득구간에서 가장 중요한 변수로 나타났다.

In this study, data mining technique was used to analyze the importance of variables in predicting the classification of health behaviors by social relations of children and youth by income category. Using jamovi and R(ver.4.1.0)/R-studio as an analysis tool, the second year (2019) data of the Korean Children and Youth Panel Survey 2018 panel data (a total of 5,197 children and youth and their guardians 5,197) was analyzed, and the results of the study are as follows. First, through mixed distribution cluster analysis, children and youth were classified into unhealthy clusters (cluster 1) and healthy clusters (cluster 2) according to the health behavior characteristics of children and youth. Eating was found to be high. Second, as a result of the t test, significant differences were found in teacher relationship, parenting attitude, time spent with parents, mother’s educational background, whether parents had breakfast, and household income. Third, it was confirmed that the social relation factors that affect the health behavior characteristics of children and youth by income bracket acted favorably in the upper income bracket. And among the social relationships, friend relationship, teacher relationship, parent relationship, and parental factors (educational background, physical activity, breakfast status), whether parents had breakfast was the most important variable in all income categories.