• Journal of the Korean Mathematical Society
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• v.59 no.3
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• pp.549-570
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• 2022

In this paper, we study the Birkhoff's ergodic theorem on geodesic metric spaces, especially on Hadamard spaces, using the notion of weighted inductive means. Also, we study a deterministic weighted sequence for the weighted Birkhoff's ergodic theorem in Hadamard spaces.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.176-184
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• 1995

In an exponential distribution, the properties of the interior and exterior trimmed means will be introduced, and reliability estimators using the two trimmed means will be compared with the UMVUE of reliability function through simulations.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.471-483
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• 2012

One of the most important problems in cluster analysis is the selection of variables that truly define cluster structure, while eliminating noisy variables that mask such structure. Brusco and Cradit (2001) present VS-KM(variable-selection heuristic for K-means clustering) procedure for selecting true variables for K-means clustering based on adjusted Rand index. This procedure starts with the fixed number of clusters in K-means and adds variables sequentially based on an adjusted Rand index. This paper presents an updated procedure combining the VS-KM with the automated K-means procedure provided by Kim (2009). This automated variable selection procedure for K-means clustering calculates the cluster number and initial cluster center whenever new variable is added and adds a variable based on adjusted Rand index. Simulation result indicates that the proposed procedure is very effective at selecting true variables and at eliminating noisy variables. Implemented program using R can be obtained on the website "http://faculty.knou.ac.kr/sskim/nvarkm.r and vnvarkm.r".

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.659-666
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• 1999

In comparing two population means, the test statistic depends on the sample means and the variances, which are very sensitive to the extremely large or small values. This paper aims at examining the behavior of such observations using proper criterion which can measure the influence of them. We derive a computationally feasible statistic which can detect influential observations on the two-sample t-statistic.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1411-1416
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• 2020

In this paper, we propose a method of extraction analysis of blood flow area on color doppler ultrasonography by using 4-directional contour tracking and K-Means algorithm. In the proposed method, ROI is extracted and a binarization method with maximum contrast as a threshold is applied to the extracted ROI. 4-directional contour algorithm is applied to extract the trapezoid shaped region which has blood flow area of brachial artery from the binarized ROI. K-Means based quantization is then applied to accurately extract the blood flow area of brachial artery from the trapezoid shaped region. In experiment, the proposed method successfully extracts the target area in 28 out of 30 cases (93.3%) with field expert's verification. And comparison analysis of proposed K-Means based blood flow area extraction on 30 color doppler ultrasonography and brachial artery blood flow ultrasonography provided by a specialist yielded a result of 94.27% accuracy on average.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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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 the Korean Data and Information Science Society
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• v.17 no.2
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• pp.301-309
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• 2006

In this paper, we show the improved fuzzy c-means clustering method. To improve, we use the double clustering as principal component analysis from objects which is located on common region of more than two clusters. In addition we use the degree of membership (probability) of fuzzy c-means which is the advantage. From simulation result, we find some improvement of accuracy in data of the probability 0.7 exterior and interior of overlapped area.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.275-277
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• 2014

본 논문에서는 세라믹 소재 영상에 가우시안 필터링 기법을 적용하여 잡음을 제거하고, K-means 알고리즘을 적용하여 결함 영역을 세분화 한 뒤, 세분화된 결함 영역에 Max-Min 이진화 기법을 이용하여 결함 영역을 추출한 후, 형태학적 기법을 이용하여 잡음을 제거하고 결함을 추출한다. 제안된 방법을 세라믹 소재 영상을 대상으로 실험한 결과, 기존의 방법보다 효율적으로 결함이 검출되는 것을 확인하였다.