• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.561-568
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• 2023

Various defects occur in concrete structures due to internal and external environments. If there is a defect, it is important to efficiently identify and maintain it because there is a problem with the structural safety of concrete. However, recent deep learning research has focused on cracks in concrete, and studies on exfoliation and contamination are lacking. In this study, focusing on exfoliation and contamination, which are difficult to label, four models were developed and their performance evaluated through unlabelling method, filtering method, the convergence of transfer learning based k-means clustering. As a result of the analysis, the convergence model classified the defects in the most detail and could increase the efficiency compared to direct labeling. It is hoped that the results of this study will contribute to the development of deep learning models for various types of defects that are difficult to label in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.110-116
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• 2023

Recently, as various examples of machine learning have been applied in the healthcare field, deep learning technology has been applied to various tasks, such as electrocardiogram examination and body composition analysis using wearable devices such as smart watches. To utilize deep learning, securing data is the most important procedure, where human intervention, such as data classification, is required. In this study, we propose a model that uses a clustering algorithm, namely, the K-means clustering, to label body fat according to gender and age considering body size aspects, such as chest circumference and waist circumference, and classifies body fat into five groups from high risk to low risk using a convolutional neural network (CNN). As a result of model validation, accuracy, precision, and recall results of more than 95% were obtained. Thus, rational decision making can be made in the field of healthcare or obesity analysis using the proposed method.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.23-36
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• 2009

Network intrusion detection have been continuously improved by using data mining techniques. There are two kinds of methods in intrusion detection using data mining-supervised learning with class label and unsupervised learning without class label. In this paper we have studied the way of improving network intrusion detection accuracy by using LBG clustering algorithm which is one of unsupervised learning methods. The K-means method, that starts with random initial centroids and performs clustering based on the Euclidean distance, is vulnerable to noisy data and outliers. The nonuniform binary split algorithm uses binary decomposition without assigning initial values, and it is relatively fast. In this paper we applied the EM(Expectation Maximization) based LBG algorithm that incorporates the strength of two algorithms to intrusion detection. The experimental results using the KDD cup dataset showed that the accuracy of detection can be improved by using the LBG algorithm.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.387-388
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• 2006

In this paper, we propose an efficient algorithm based on PCA and LDA combining K-means clustering method, which has better accuracy of face recognition than Eigenface and Fisherface. In this algorithm, PCA is firstly used to reduce the dimensionality of original face image. Secondly, a truncated face image data are sub-clustered by K-means clustering method based on Euclidean distances, and all small subclusters are labeled in sequence. Then LDA method project data into low dimension feature space and group data easier to classify. Finally we use nearest neighborhood method to determine the label of test data. To show the recognition accuracy of the proposed algorithm, we performed several simulations using the Yale and ORL (Olivetti Research Laboratory) database. Simulation results show that proposed method achieves better performance in recognition accuracy.

• Journal of Korea Multimedia Society
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• v.22 no.4
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• pp.480-490
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• 2019

Nowadays, Electronic Medical Record (EMR) has just implemented at few hospitals for Outpatient Department (OPD). OPD is the diversified data, it includes demographic and diseases of patient, so it need to be clustered in order to explore the hidden rules and the relationship of data types of patient's information. In this paper, we propose a novel approach for unsupervised clustering of patient's demographic and diseases in OPD. Firstly, we collect data from a hospital at OPD. Then, we preprocess and transform data by using powerful techniques such as standardization, label encoder, and categorical encoder. After obtaining transformed data, we use some strong experiments, techniques, and evaluation to select the best number of clusters and best clustering algorithm. In addition, we use some tests and measurements to analyze and evaluate cluster tendency, models, and algorithms. Finally, we obtain the results to analyze and discover new knowledge, meanings, and rules. Clusters that are found out in this research provide knowledge to medical managers and doctors. From these information, they can improve the patient management methods, patient arrangement methods, and doctor's ability. In addition, it is a reference for medical data scientist to mine OPD dataset.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.81-86
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• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.800-807
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• 2017

The Pattern Sequence-based Forecasting (PSF) is an approach to forecast the behavior of time series based on similar pattern sequences. The innovation of PSF method is to convert the load time series into a label sequence by clustering technique in order to lighten computational burden. However, it brings about a new problem in determining the number of clusters and it is subject to insufficient similar days occasionally. In this paper we proposed an adaption of the PSF method, which introduces a new clustering index to determine the number of clusters and imposes a threshold to solve the problem caused by insufficient similar days. Our experiments showed that the proposed method reduced the mean absolute percentage error (MAPE) about 15%, compared to the PSF method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.202-203
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• 2009

In this paper, system divide into the edge extraction, labeling, clustering, and South Korea's traditional color combination and identifying to identify Korea's traditional colors. Edge is extracted using Canny operator. And given the label, and clustering to ensure the quality of the cluster. Finally, we identify color harmony by organizing and comparing primary color with secondary color configuration table Korea traditional color.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.155-164
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• 2024

This study proposes an unsupervised learning-based clustering model to estimate the ESG ratings of domestic public institutions. To achieve this, the optimal number of clusters was determined by comparing spectral clustering and k-means clustering. These results are guaranteed by calculating the Davies-Bouldin Index (DBI), a model performance index. The DBI values were 0.734 for spectral clustering and 1.715 for k-means clustering, indicating lower values showed better performance. Thus, the superiority of spectral clustering was confirmed. Furthermore, T-test and ANOVA were used to reveal statistically significant differences between ESG non-financial data, and correlation coefficients were used to confirm the relationships between ESG indicators. Based on these results, this study suggests the possibility of estimating the ESG performance ranking of each public institution without existing ESG ratings. This is achieved by calculating the optimal number of clusters, and then determining the sum of averages of the ESG data within each cluster. Therefore, the proposed model can be employed to evaluate the ESG ratings of various domestic public institutions, and it is expected to be useful in domestic sustainable management practice and performance management.