• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06c
• /
• pp.88-90
• /
• 2006

최근 정보 통신기술의 발전으로 인하여 전자상거래가 확산되고 있는 실정이며, 이용하는 고객 또한 상당히 증가하고 있다. 고객의 활발한 구매 거래 활동으로 하루에도 아주 많은 양의 데이터가 생성되고 있는 실정이다. 이에 전자상거래의 웹 사이트 관리자나 경영자는 고객의 구매형태나 패턴의 특징을 파악하여 보다 효율적인 서비스를 고객에게 제공하기 위하여 현재까지 유사그룹의 고객 세분화를 적용하는 연구가 이루어지고 있다. 본 논문에서는 전자상거래에서 고객들의 정보를 분석하여 개인화하기 위한 방법으로 사용되는 고객 프로파일을 이용하여 고객세분화 하는데 적용을 하고자 한다. 기존 고객세분화의 통계적인 분석이 아닌 XML 기반의 고객 정보를 XPath를 이용하여 고객세분화에 필요한 규칙을 생성하고, 그 규칙을 바탕으로 고객 프로파일을 생성하는 방법과 프로파일을 이용한 군집에 따른 분석 결과 및 추천서비스를 소개하고자 한다.

• The Korean Journal of Applied Statistics
• /
• v.29 no.4
• /
• pp.613-625
• /
• 2016

The development of technology has changed the world drastically. Patent data analysis helps to understand modern technology trends and predict prospective future technology. In this paper, we analyze the patent citation network using the USPTO data between 1985 and 2012 to identify technology trends. We use network centrality measures that include a PageRank algorithm to find core technologies and identify groups of technology with similar properties with statistical network models.

• Korean Journal of Social Welfare
• /
• v.57 no.2
• /
• pp.205-230
• /
• 2005

This study empirically examines if there is a certain linkage between the production regimes and welfare systems; and if linked, how they are linked. It also investigates what the different regimes performed in terms of economic growth and redistribution. As a matter of fact, we have a series of studies that explores structural diversity of production and welfare. However, the existing studies are limited in that they consider only specific facets of the structure, although the structure of welfare capitalism should be studied as a comprehensive whole. This is the gap which this study tries to overcome. The study is composed of two major parts. The first one is the cluster analysis that examines if Esping-Andersen's notion about three different welfare regime and the thesis of diversity of capitalism can be dealt within a single research framework. The second is the ANOVA analysis investigating if variables of production and welfare are to be statistically different in the trichotomy framework. According to the result of the analyses, we can find at least two important evidences about institutional complementaries of production and welfare. First, Esping-Andersen's framework is useful to comprehensively deal with production as well as welfare. Secondly, there are statistically different regimes of production and welfare in the context of political economic and social policy variables. What is the most striking conclusion of the study is that there is no difference among the regimes in terms of the level of economic efficiency; while we can find a huge differences in terms of the level of welfare effectiveness. In conclusion, there is no substantive evidence to argue that welfare is innately antithesis of economic growth.

• The Journal of the Korea Contents Association
• /
• v.15 no.3
• /
• pp.114-122
• /
• 2015

This study aims to analyze the satisfaction of seniors living in Cheonan City downtown as to their residential environment. Also, this study intends to identify which factors should be improved first to make downtown a favorable residential area. To that end, 'social indicators of Chungnam' was used. The collected data was analyzed through a statistical analysis method using ANOVA (analysis of variance) and a cluster analysis. It led to the following findings. First, 6.9% of the elderly residents expressed their wish to move from their downtown residence. Thus, the majority of the residents do not want to move. Second, the satisfaction of the elderly residents in their downtown residence scored 6.09. The score is higher than those of other regions. Thus, it is highly possible to develop downtown into a senior-friendly area. Third, as for satisfaction in downtown residence, it was higher among the following groups: men; those with high school or higher level of education; those earning at least a million won a month; family of one generation. Fourth, satisfaction in the following factors was relatively low: culture and education; interaction with neighbors and trust in them; car accidents. Thus, those factors should be improved for downtown residents. Above all, community-faced facilities should be expanded to increase exchanges with neighbors and trust in them. To attract women dissatisfied with downtown residence. it is imperative to increase daily safety by reducing car accidents and crime.

• The Korean Journal of Applied Statistics
• /
• v.30 no.6
• /
• pp.889-899
• /
• 2017

Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.

• Journal of Navigation and Port Research
• /
• v.45 no.3
• /
• pp.95-101
• /
• 2021

Marine Incidents based on Heinrich's law are very important in preventing accidents. However, marine Incident data are mainly qualitative and are used to prevent similar accidents through case sharing rather than statistical analysis, which can be confirmed in the marine Incident-related data posted in the Korea Maritime Safety Tribunal. Therefore, this study derived quantitative results by analyzing the causes of marine incidents during berthing using various methods of statistical analysis. To this end, data involving marine incidents from various shipping companies were collected and reclassified for easy analysis. The main keywords were derived via primary analysis using text mining. Only meaningful words were selected via verification by an expert group, and time series and cluster analysis were performed to predict marine incidents that may occur during berthing. Although the role of an expert group was still required during the analysis, it was confirmed that quantitative analysis of marine incidents was feasible, and iused to provide cause and accident prevention information.

• Investigative Magnetic Resonance Imaging
• /
• v.6 no.2
• /
• pp.137-146
• /
• 2002

Purpose : This paper introduces an improved classification approach which adopts a deterministic relaxation method and an agglomerative clustering technique for the classification of MRI using neural network. The proposed approach can solve the problems of convergency to local optima and computational burden caused by a large number of input patterns when a neural network is used for image classification. Materials and methods : Application of Hopfield neural network has been solving various optimization problems. However, major problem of mapping an image classification problem into a neural network is that network is opt to converge to local optima and its convergency toward the global solution with a standard stochastic relaxation spends much time. Therefore, to avoid local solutions and to achieve fast convergency toward a global optimization, we adopt MFA to a Hopfield network during the classification. MFA replaces the stochastic nature of simulated annealing method with a set of deterministic update rules that act on the average value of the variable. By minimizing averages, it is possible to converge to an equilibrium state considerably faster than standard simulated annealing method. Moreover, the proposed agglomerative clustering algorithm which determines the underlying clusters of the image provides initial input values of Hopfield neural network. Results : The proposed approach which uses agglomerative clustering and deterministic relaxation approach resolves the problem of local optimization and achieves fast convergency toward a global optimization when a neural network is used for MRI classification. Conclusion : In this paper, we introduce a new paradigm to classify MRI using clustering analysis and deterministic relaxation for neural network to improve the classification results.

• Journal of KIISE:Software and Applications
• /
• v.36 no.12
• /
• pp.1016-1023
• /
• 2009

A license plate recognition system consists of image processing in which characters and features are extracted, and pattern recognition in which extracted characters are classified. Feature extraction plays an important role in not only the level of data reduction but also performance of recognition. Thus, in this paper, we focused on the recognition of numeral characters especially on the feature extraction of numeral characters which has much effect in the result of plate recognition. We suggest a hybrid statistical feature model which assures the best dispersion of input data by reassignment of clustering property of input data. And we verify the effectiveness of suggested model using multi-layer perceptron and learning vector quantization neural networks. The results show that the proposed feature extraction method preserves the information of a license plate well and also is robust and effective for even noisy and external environment.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.10 no.4
• /
• pp.60-74
• /
• 2007

For quantitative analysis of spatio-temporal distribution pattern on marine environment, spatial autocorrelation statistics on the both global and local aspects was applied to the observed data obtained from Gwangyang Bay in South Sea of Korea. Global indexes such as Moran's I and General G were used for understanding environmental distribution pattern in the whole study area. LISAs (local indicators of spatial association) such as Moran's I ($I_i$) and $G_i{^*}$ were considered to find similarity between a target feature and its neighborhood features and to detect hot spot and/or cold spot. Additionally, the significance test on clustered patterns by Z-scores was carried out. Statistical results showed variations of spatial patterns quantitatively in the whole year. Then all of general water quality, nutrients, chlorophyll-a and phytoplankton had strong clustered pattern in summer. When global indexes showed strong clustered pattern, the front region with a negative $I_i$ which means a strong spatial variation was observed. Also, when global indexes showed random pattern, hot spot and/or cold spot were/was found in the small local region with a local index $G_i{^*}$. Therefore, global indexes were useful for observing the strength and time series variations of clustered patterns in the whole study area, and local indexes were useful for tracing the location of hot spot and/or cold spot. Quantification of both spatial distribution pattern and clustering characteristics may play an important role to understand marine environment in depth and to find the reasons for spatial pattern.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.5
• /
• pp.679-683
• /
• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.