• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• Journal of information and communication convergence engineering
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• 제6권4호
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• pp.434-438
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• 2008

Massive data are continuously produced with a data rate of over several terabytes every day. These applications need effective clustering algorithms to achieve an overall high performance computation. In this paper, we propose ancestor as cluster center based approach to clustering, the K-means algorithm using ancestor. We modify the K-means algorithm. We present a clustering architecture and a clustering algorithm that minimize of I/Os and show a performance with excellent. In our experimental performance evaluation, we present that our algorithm can improve the I/O speed and the query processing time.

• 정보관리학회지
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• 제18권2호
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• pp.203-230
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• 2001

본 연구에서는 문헌을 기반으로 한 지식의 자동분류를 위해 최적의 클러스터링 모형을 제시하고자 하였다. 클러스터링 실험을 위해서 신문기사 실험집단과 학술논문 초록 실험집단을 구축하였고, 분류 성능 평가 척도인 WACS를 개발하였다. 분류자질로 사용한 용어의 집합은 다양한 자질 축소 기준을 적용하여 생성하였으며, 다양한 용어 가중치를 사용하였다. 유사계수 공식으로는 코사인 계수와 자카드 계수를 적용하였으며, 클러스터링 알고리즘으로는 비계층적 기법인 완전연결 기법과 계층적 기법인 K-means기법을 각각 사용하였다. 실험 결과 신문기사 원문 집단에서의 성능이 좋았으며, 완전연결 기법의 성능이 K-means 기법보다 높게 나타났다. 역문헌빈도의 적용은 완전연결 클러스터링에서는 긍정적인 효과가 나타났으나, K-means 클러스터링에서는 그렇지 못했다. 분류자질은 전체의 7.66%만 사용하였을 경우에도 성능 저하가 크지 않았으며, K-means 클러스터링에서는 오히려 성능 향상 효과가 있었다.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1438-1444
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• 2018

Recently, with the development of Internet technologies and propagation of smart devices, use of microblogs such as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new information on microblogs because the content on their timelines is continually updating. Therefore, clustering algorithms are necessary to arrange the content of microblogs by grouping them for a user who wants to get the newest information. However, microblogs have word limits, and it has there is not enough information to analyze for content clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not only measures the similarity between the data represented as a vector space model, but also measures the semantic similarity between the data by exploiting the TagCluster for clustering. Through the experimental results on the RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clustering algorithm.

• Wind and Structures
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• 제36권6호
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• pp.405-421
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• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

• 전기학회논문지
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• 제66권5호
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• 한국인터넷방송통신학회논문지
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• 제13권1호
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• pp.83-88
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• 2013

본 연구의 목적은 유사 블로그 추천 시스템을 통해서 특정 주제의 유사도에 따라 주제를 찾아 주는 것이다. 유사 추천 시스템을 실현하기 위해서는 대규모 데이터 집합에서 유사항목을 가진 그룹을 찾을 수 있도록 군집해야 한다. 군집화(clustering) 기법은 군집하고자 하는 목적에 따라 적합한 기법과 군집수가 결정되어야 한다. 군집기법으로는 가장 많이 사용되는 K-means 알고리즘을 사용 하였고 추천 알고리즘은 흰개미 군집 알고리즘을 사용하였다. 흰개미 습성 모델을 이용한 군집화 기법은 K-means 알고리즘이 갖고 있는 적절한 군집 갯수 문제점을 해결하고, 군집화 시간을 단축하며, 군집을 위한 군집 평균 이동횟수를 개선한다.

• 응용통계연구
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• 제22권4호
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• pp.723-733
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• 2009

K-평균 군집분석이 가지는 두 가지 근본적인 어려움은 사전에 미리 군집 수를 정해야 하는 문제와 초기 군집중심에 따라 결과가 달라질 수 있는 문제이다. 본 연구에서는 이러한 문제를 해결하기 위한 자동화 K-평균 군집분석 절차를 제안하고, R을 이용하여 구현한 결과를 제공한다. 자동화 K-평균 군집분석에서 제안된 절차는 처음 단계로서 계층적 군집분석을 행한 후 이를 이용하여 군집 수와 초기 군집수를 자동으로 정하고, 다음 단계로 이 결과를 이용하여 K-평균 군집분석을 수행하는 방법을 택하였다. 처음 단계에서 이용된 계층적 군집분석 방법으로는 Ward의 군집분석을 한 후에 Mojena의 규칙을 이용하여 군집 수를 정하는 방법을 택하거나, 모형근거 군집분석방법을 수행한 후에 BIC 값을 이용하여 군집 수를 정하는 방법을 이용하였다. 제안된 자동화 K-평균 군집절차에는 대량자료의 분석에도 용이하게 이용될 수 있도록 반복된 표본추출 방법을 이용하여 군집 수 및 군집 중심을 구하는 절차를 포함하였다. 구현된 R 프로그램은 www.knou.ac.kr/ sskim/autokmeans.r에서 제공하고 있다.

• 한국멀티미디어학회논문지
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• 제23권1호
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• pp.1-7
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• 2020

Clustering methods essentially take a two-step approach; extracting feature vectors for dimensionality reduction and then employing clustering algorithm on the extracted feature vectors. However, for clustering images, the traditional clustering methods such as stacked auto-encoder based k-means are not effective since they tend to ignore the local information. In this paper, we propose a method first to effectively reduce data dimensionality using convolutional auto-encoder to capture and reflect the local information and then to accurately cluster similar data samples by using a hierarchical clustering approach. The experimental results confirm that the clustering results are improved by using the proposed model in terms of clustering accuracy and normalized mutual information.