• Journal of the Korean Home Economics Association
• /
• v.36 no.3
• /
• pp.47-60
• /
• 1998

This Pupose of this study was to identify the type of attitudes toward money to investigate the relationship between the attitued and consumption expenditure pattern. Data were obtained from 398 household living in Seoul. Factor analysis was used for examining dimensions of attitudes toward money and cluster analysis for classifying the households by money attitudes. This study found five money attitude dimensions, i.e., the Means of Success dimension, the Means of Pleasure dimension, the Means of Security dimension, the Symbol of Anxiety dimension, and the Parsimony dimension, Based on the variation in the dimensions, five money types were identified, ie., the Means of Success, the Means of Pleasure, the Means of Security, the Symbol of Anxiety, and the Parsimony.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2002.11a
• /
• pp.373-376
• /
• 2002

K-Means 알고리즘은 재배치 기법의 일종으로 K 개의 초기 클러스터중심(centroid)를 중심으로 K 개의 클러스터가 될 때까지 클러스터링을 반복하는 것이다. K-Means 알고리즘은 특성상 초기 클러스터 중심과 새롭게 생성된 클러스터 중심에 따라 클러스터링 결과가 달라진다. 본 논문에서는 K-Means Algorithm 의 초기 클러스터중심 선택 방법과 새로운 클러스터 중심 결정 방법을 개선한 변형 K-Means Algorithm을 제안한다. SMART 시스템에서 제안한 16가지 가중치 계산 방식에 의하여 두 알고리즘의 성능을 평가한 결과 제안한 변형 알고리즘이 재현률과 F-Measure 에서 20%이상 향상된 결과를 얻을 수 있었으며 특정 주제 아래 문서가 할당되는 클러스터링 성능이 우수하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.750-752
• /
• 2014

K-Means 알고리즘은 구현이 쉽고, 패턴수가 n일 때 시간 복잡도가 O(n)인 장점을 가져 대용량 데이터에서 널리 이용된다. 그러나, K-Means 알고리즘은 초기 클러스터 중심을 어떻게 선정하는가에 따라 할당-재계산 횟수, 클러스터링 결과를 결정짓는다. 본 논문에서는 K-Means 알고리즘에서 클러스터 초기 중심 선정 연구를 살펴보고 계통임의추출법을 적용하여 K-Means 초기 중심 선정 방법을 제안한다. 제안한 방법은 대용량 데이터의 클러스터링 시간을 감소하고 정확도를 향상시킬 수 있다.

• International Journal of Computer Science & Network Security
• /
• v.23 no.7
• /
• pp.32-38
• /
• 2023

This study deals with arbitration award via modern technical means; because e-Arbitration is deemed to be one of the most important substitute means for the settlement of disputes arising from electronic transactions. This type of arbitration is characterized by fast settlement of disputes, as well as fast enforcement of awards rendered thereon. The researcher seeks to indicate the content of the award, the conditions for rendering it, and to analyze the legal provisions related to its legal basis in the Saudi Law of Arbitration. This study shows that an arbitration award, rendered via modern technical means has a number of advantages, such as fast settlement, less cost, and keeping pace with modern technology, which is an aim of Saudi Arabia Vision 2030. The study also points out certain problems facing arbitration via technical means; however, the most important of which is the insufficiency of some legal rules associated with traditional arbitration, as contained in the Saudi Law of Arbitrator, which are incompatible with or applicable to an arbitration award which is rendered via modern means.

• Communications of the Korean Mathematical Society
• /
• v.24 no.4
• /
• pp.553-559
• /
• 2009

In the paper, some new inequalities for certain bivariate means are obtained, which extend some known results.

• The Journal of Korean Academy of Prosthodontics
• /
• v.26 no.1
• /
• pp.133-151
• /
• 1988

The objectives of this study were to compare the position of centric relation guided by means of leaf gauges, bilateral manipulation, chin-point guidance with Lucia jig and self-guided method and the reproducibility, respectively. A Veri-check (Denar Co., Anaheim, California) was employed for examining, and the displacement of position and reproducibility were verified. The following results were obtained. 1. On the sagittal plane, the centric relation guided by means of leaf gauges showed greater posterior displacement than that by means of bilateral manipulation and less than that by means of chin-point guidance with Lucia jig, and self-guided centric relation showed least posterior displacement. The centric relation guided by means of bilateral manipulation showed greater superior displacement than that by means of chin-point guidance with Lucia jig and less than that by means of leaf gauges and self-guided centric relation showed least superior displacement. 2. On the horizontal plane, the centric relation guided by means of chin-point guidance with Lucia jig showed greater posterior displacement than that of bilateral manipulation and less than that leaf gauges, however the self-guided centric relation showed slightly anterior displacement. 3. The anteroposterior displacement measured on sagittal plane and horizontal plane were highly correlated. (p<0.05). 4. The reproducibility of centric relation guided by means of leaf gauges, bilateral manipulation and chin-point guidance with Lucia jig were similar and more reproducible than self-guided centric relation.

• The Journal of the Korea Contents Association
• /
• v.9 no.1
• /
• pp.106-114
• /
• 2009

In this paper we proposed a fast method for a K-Means Clustering algorithm. The main characteristic of this method is that it uses precalculated data which possibility of change is high in order to speed up the algorithm. When calculating distance to cluster centre at each stage to assign nearest prototype in the clustering algorithm, it could reduce overall computation time by selecting only those data with possibility of change in cluster is high. Calculation time is reduced by using the distance information produced by K-Means algorithm when computing expected input data whose cluster may change, and by using such distance information the algorithm could be less affected by the number of dimensions. The proposed method was compared with original K-Means method - Lloyd's and the improved method KMHybrid. We show that our proposed method significantly outperforms in computation speed than Lloyd's and KMHybrid when using large size data which has large amount of data, great many dimensions and large number of clusters.

• The Korean Journal of Applied Statistics
• /
• v.23 no.3
• /
• pp.595-607
• /
• 2010

This paper addresses the problem of reducing the speckle noise in SAR images by wavelet transformation, using a non-local means(NLM) filter originated for Gaussian noise removal. Log-transformed SAR image makes multiplicative speckle noise additive. Thus, non-local means filtering and wavelet thresholding are used to reduce the additive noise, followed by an exponential transformation. NLM filter is an image denoising method that replaces each pixel by a weighted average of all the similarly pixels in the image. But the NLM filter takes an acceptable amount of time to perform the process for all possible pairs of pixels. This paper, also proposes an alternative strategy that uses the t-test more efficiently to eliminate pixel pairs that are dissimilar. Extensive simulations showed that the proposed filter outperforms many existing filters terms of quantitative measures such as PSNR and DSSIM as well as qualitative judgments of image quality and the computational time required to restore images.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.2
• /
• pp.92-98
• /
• 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
• /
• v.24 no.11
• /
• pp.1411-1416
• /
• 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.