• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.1-8
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• 2007

In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.43-50
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• 2010

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are the two most well-known clustering algorithms in fuzzy clustering area, and have been applied in many applications in their original or modified forms. However, FCM's noise sensitivity problem and PCM's overlapping cluster problem are also well known. Recently there have been several attempts to combine both of them to mitigate the problems and possibilistic fuzzy c-means (PFCM) showed promising results. In this paper, we proposed a modified PFCM using regularization to reduce noise sensitivity in PFCM further. Regularization is a well-known technique to make a solution space smooth and an algorithm noise insensitive. The proposed algorithm, PFCM with regularization (PFCM-R), can take advantage of regularization and further reduce the effect of noise. Experimental results are given and show that the proposed method is better than the existing methods in noisy conditions.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.245-253
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• 2011

Due to the imprudent spending of the fossil fuels, the environment was contaminated seriously and the exhaustion problems of the fossil fuels loomed large. Therefore people become taking a great interest in alternative energy resources which can solve problems of fossil fuels. The wind power energy is one of the most interested energy in the new and renewable energy. However, the plants of wind power energy and the traditional power plants should be balanced between the power generation and the power consumption. Therefore, we need analysis and prediction to generate power efficiently using wind energy. In this paper, we have performed a research to predict power generation patterns using the wind power data. Prediction approaches of datamining area can be used for building a prediction model. The research steps are as follows: 1) we performed preprocessing to handle the missing values and anomalous data. And we extracted the characteristic vector data. 2) The representative patterns were found by the MIA(Mean Index Adequacy) measure and the SOM(Self-Organizing Feature Map) clustering approach using the normalized dataset. We assigned the class labels to each data. 3) We built a new predicting model about the wind power generation with classification approach. In this experiment, we built a forecasting model to predict wind power generation patterns using the decision tree.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.195-204
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• 2023

In this study, prompt engineering that improves prompts was explored to improve the performance of teaching and learning materials recommendations using generative artificial intelligence such as GPT and Stable Diffusion. Picture materials were used as the types of teaching and learning materials. To explore the impact of the prompt composition, a Zero-Shot prompt, a prompt containing learning target grade information, a prompt containing learning goals, and a prompt containing both learning target grades and learning goals were designed to collect responses. The collected responses were embedded using Sentence Transformers, dimensionalized to t-SNE, and visualized, and then the relationship between prompts and responses was explored. In addition, each response was clustered using the k-means clustering algorithm, then the adjacent value of the widest cluster was selected as a representative value, imaged using Stable Diffusion, and evaluated by 30 elementary school teachers according to the criteria for evaluating teaching and learning materials. Thirty teachers judged that three of the four picture materials recommended were of educational value, and two of them could be used for actual classes. The prompt that recommended the most valuable picture material appeared as a prompt containing both the target grade and the learning goal.

• Journal of Navigation and Port Research
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• v.28 no.3
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• pp.185-191
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• 2004

The mean and Clustering are important methods of data mining, which is now widely applied to various multi-attributes problem However, feature weighting and feature selection are important in those methods bemuse features may differ in importance and such differences need to be considered in data mining with various multiful-attributes problem. In addition, in the event of arithmetic mean, which is inadequate to figure out the most fitted result for structure of evaluation with attributes that there are weighted and ranked. Moreover, it is hard to catch hold of a specific character for assume the form of user's group. In this paper. we propose a dispersion mean algorithm for evaluation of similarity measure based on the geometrical figure. In addition, it is applied to mean classified by user's group. One of the key issues to be considered in evaluation of the similarity measure is how to achieve objectiveness that it is not change over an item ranking in evaluation process.

• Journal of Internet of Things and Convergence
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• v.3 no.2
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• pp.1-5
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• 2017

As CCTV technology develops, it is used in various fields. Currently, we want to know about CCTV operation in detail. In addition, CCTV in many fields is causing problems in operation. We plan to design a new system to solve the problem. In this paper, we analyze data using K-means so that CCTV can be operated efficiently, add new technology and function to existing system to increase image technology and operate efficiently, Technology. In addition, we will design a new system for CCTV technology using k-means so that the CCTV can be efficiently operated in the center, and propose the problem to solve the problem.

• Asian Journal of Business Environment
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• v.12 no.1
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• pp.11-16
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• 2022

Purpose: This study is aimed to map the provinces in Indonesia based on the education and ICT indicators using several unsupervised learning algorithms. Research design, data, and methodology: The education and ICT indicators such as student-teacher ratio, illiteracy rate, net enrolment ratio, internet access, computer ownership, are used. Several approaches to get deeper understanding on provincial strength and weakness based on these indicators are implemented. The approaches are Ensemble K-Mean and Fuzzy C Means clustering. Results: There are at least three clusters observed in Indonesia the education quality, participation, facilities and ICT Access. Cluster with high education quality and ICT access are consist of DKI Jakarta, Yogyakarta, Riau Islands, East Kalimantan and Bali. These provinces show rapid economic growth. Meanwhile the other cluster consisting of six provinces (NTT, West Kalimantan, Central Sulawesi, West Sulawesi, North Maluku, and Papua) are the cluster with lower education quality and ICT development which impact their economic growth. Conclusions: The provinces in Indonesia are clustered into three group based on the education attainment and ICT indicators. Some provinces can directly implement e-learning; however, more provinces need to improve the education quality and facilities as well as the ICT infrastructure before implementing the e-learning.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.61-69
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• 2011

This paper proposes a new algorithm that improve color component of compensated image using Retinex method for back-light image. A back-light image has two regions, one of the region is too bright and the other one is too dark. If an back-light image is improved contrast using Retinex method, it loses color information in the part of brightness of the image. In order to make up loss information, proposed algorithm adds color components from original image. The histogram can be divided three parts that brightness, darkness, midway using K-mean (k=3) algorithm. For the brightness, it is used color information of the original image. For the darkness, it is converted using by Retinex method. The midway region is mixed between original image and Retinex result image in the ratio of histogram. The ratio is determined by distance from dark area. The proposed algorithm was tested on nature back-light images to evaluate performance, and the experimental result shows that proposed algorithm is more robust than original Retinex algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.94-97
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• 2009

연 및 계절강수량의 정확한 예보는 수자원관리에 매우 중요하다. 예보 정확도를 높이기 위한 다양한 연구가 계속 진행되어 왔다. 그럼에도 불구하고 강수자료가 가지는 매우 큰 불확실성 때문에 예보의 정확도 향상은 계속되는 숙제로 우리에게 남아 있다. 이를 개선하기 위하여 본 연구에서는 군집화 기법을 이용한 연 및 계절 강수량 예측개선에 대한 연구 결과를 제시하였다. 이를 위하여 연강수량, 계절강수량 및 월강수량의 예측을 위하여 전구에서 일어나는 각종 기후 인자들과의 상관성 분석은 대단히 중요하다. 전 세계적으로 어느 특정 지역에서의 선행 기후인자 변화 양상이 우리나라의 강수량에 높은 상관성을 가지며 영향을 미친다면 예측을 위한 매우 유용한 정보라 하겠으나 국내 강수량과 기후 지수 사이의 선형 상관성은 매우 낮을 뿐만 아니라 지체상관성도 특정 지체에서 매우 큰 상관성을 보이는 인자를 찾기 어려움을 알 수 있다. 이를 극복하기 위하여 본 연구에서는 k-mean clustering을 이용하여 우리나라 주변의 기후조건을 분류하고 기후조건에 따른 강수량의 변화를 분석하였다. 남중국해역($105^{\circ}E\;^{\sim}\;135^{\circ}E$, $0^{\circ}N\;^{\sim}\;35^{\circ}N$), 우리나라 연안 해역 ($110^{\circ}E\;^{\sim}\;150^{\circ}E$, $20^{\circ}N\;^{\sim}\;40^{\circ}N$), 인도양 해역 ($75^{\circ}E\;^{\sim}\;105^{\circ}E$, $0^{\circ}N\;^{\sim}\;25^{\circ}N$) 및 아라비아 해역 ($45^{\circ}E\;^{\sim}\;75^{\circ}E$, $0^{\circ}N\;^{\sim}\;30^{\circ}N$ 평균 해수면 온도 변화에 따라 8개 군집으로 분류한 분석결과로 분석결과 2008년도는 그룹 5에 해당하며 그룹 5의 기후 상태는 근해와 남중국해역의 평균 해수면 온도가 평년보다 낮고 인도양 해역과 아라비아 해역의 평균 해수면 온도는 평년값과 비슷한 상태를 나타낸다. 그룹 5에 해당하는 기후조건에서 차년의 강수평균은 평년값 보다 적음을 보였다. 이러한 특성은 전체 유역에 걸쳐 동일하게 나타났다. 이에 대한 계절적 평균 분포는 군집 5에 대한 차년도 강수의 평균 계절분포는 전체적으로 평년값보다 낮게 나타났다. 이에 근거하여 올해 연 평균 강수량은 평년값보다 적을 것이며 전체 계절에 대하여도 평년값보다 적은 강수량이 올 것으로 판단된다. 이는 기상청의 2009년 봄철 기후전망과 유사한 예측 결과를 보여준다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.374-379
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• 2007

This paper focuses on the improvement of fault detection algorithm in FRTU(feeder remote terminal unit) on the feeder of distribution power system. FRTU is applied to fault detection schemes for phase fault and ground fault. Especially, cold load pickup and inrush restraint functions distinguish the fault current from the normal load current. FRTU shows FI(Fault Indicator) when the fault current is over pickup value or inrush current. STFT(Short Time Fourier Transform) analysis provides the frequency and time Information. FCM(Fuzzy C-Mean clustering) algorithm extracts characteristics of harmonics. The neural network system as a fault detector was trained to distinguish the inruih current from the fault status by a gradient descent method. In this paper, fault detection is improved by using FCM and neural network. The result data were measured in actual 22.9kV distribution power system.