• Asia pacific journal of information systems
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• v.26 no.1
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• pp.66-79
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• 2016

The availability of detailed data on customers' online behaviors and advances in big data analysis techniques enable us to predict consumer behaviors. In the past, researchers have built purchase prediction models by analyzing clickstream data; however, these clickstream-based prediction models have had several limitations. In this study, we propose a new method for purchase prediction that combines information theory with machine learning techniques. Clickstreams from 5,000 panel members and data on their purchases of electronics, fashion, and cosmetics products were analyzed. Clickstreams were summarized using the 'entropy' concept from information theory, while 'random forests' method was applied to build prediction models. The results show that prediction accuracy of this new method ranges from 0.56 to 0.83, which is a significant improvement over values for clickstream-based prediction models presented in the past. The results indicate further that consumers' information search behaviors differ significantly across product categories.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.247-252
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• 2006

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn until signal is growing to abnormal state that the signal is over or under the set point. therefore cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without any additional sensors. By analyzing the data with high correlation coefficient(CC), correlation level of interactive data can be defined. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC. FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.851-858
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• 2009

In the last decades, there has been much interest in climate variability because its change has dramatic effects on humanity. Especially, the precipitation data are measured over space and their spatial association is so complicated. So we should take into account such a spatial dependency structure while analyzing the data. However, in linear models for analyzing the data, data sets show severely skewed distribution. In the paper, we consider the Box-Cox transformation to satisfy the normal distribution prior to the analysis, and employ a Bayesian hierarchical framework to investigate the spatial patterns. The data set we considered is monthly average precipitation of the third quarter of 2007 obtained from 347 automated monitoring stations in Contiguous South Korea.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.281-286
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• 2005

Almost ship monitoring systems are event driven alarm system which warn only when the measurement value is over or under set point. These kinds of system cannot warn while signal is growing to abnormal state until the signal is over or under the set point and cannot play a role for preventive maintenance system. This paper proposes fault diagnosis method which is able to diagnose and forecast the fault from present operating condition by analyzing monitored signals with present ship monitoring system without additional sensors. By analyzing this data having high correlation coefficient(CC), correlation level of interactive data can be understood. Knowledge base of abnormal detection can be built by referring level of CC(Fault Detection CC, FDCC) to detect abnormal data among monitored data from monitoring system and knowledge base of diagnosis built by referring CC among interactive data for related machine each other to diagnose fault part.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.2
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• pp.139-146
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• 2019

The purpose of this study is to derive an improved method for analyzing old buildings with risk of collapse using public big data. Previous studies on the risk of building collapse focused on internal factors such as building age and structural vulnerability. However, this study suggests a method to derive potentially collapsible buildings considering not only internal factors of buildings but also external factors such as nearby new construction data. Based on the big data analysis, this study develops a system to visualize vulnerable buildings that require safety diagnosis and proposed a future utilization plan.

• Journal of the Korean Home Economics Association
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• v.40 no.2
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• pp.67-86
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• 2002

The purpose of this research is to offer a method to derive the body shapes and pose factors with resets from analyzing factors by using the measurement values of human body. 297 male subjects in their twenties participated in this study. 54 anthropometric and 35 photographic measurements were taken from each subject. Data is the several typical items selected among items and you have to make an index value by using these typical items and convert it. The index has high correlation with each item, is easy to be measured and is to be used after selecting an important item in designing prototype. With results of analyzing reliability level by each item, correlation and items that factor loading is low, the item that the correlation is high is to be removed within the range that maintains the reliability level. Five factors of the front part and seven factors of the profile came to be derived from this kind of process.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.627-632
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• 1994

This study suggests the contingency framework understanding the implementation of NIMT and conducts empirical analysis using survey data. The main objectives of the thesis are as follows : (1) analyzing the relationship between adaptation requirements and organizational adaptation efforts. (2) analyzing the relationship between organizational adaptation efforts and implementation performance, and the moderating effect of the adaptation requirements. (3) analyzing the relationship between organizational adaptation efforts and organization characteristics(organization size, technical capability, top management support). A field study was undertaken to test the hypothesized relationships among adaptation efforts, adaptations requirements, organization characteristics, implementation performance. Data were collected from 52 NMT implementation projects of 45 machine tool, metal component firms. The emprical relflts indicated that adaptation requirrnents, organization characteristics were significantly related to organizational adaptation efforts and adaptation efforts can significantly improve implementation perfomance, but the moderating effect of adaptation requirements on adaptation efforts and implementation performance was not supported.

• Journal of The Korean Association For Science Education
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• v.15 no.4
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• pp.452-458
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• 1995

Science textbooks are very important materials in order to know elementary science learning in Japan and Korea. In this research the 5th grade science textbooks in Japan and Korea are analyzed by an analyzing category. The analyzing category is consisted of knowledge and scientific inquiry. Knowledge is divided by fact, concept, and rule. Scientific inquiry is divided by problem cognition, variable control, experiment planning, observing, measuring, categorizing, inferring, data transformation, predicting, correlation, cause and effect, result, communication, which are 13 subcategories. Analyzing methods are counting the frequency of each subcategory and tabulating the data. The results of this study are: 1. The frequency of scientific inquiry appeared in Korean 5th grade science textbooks is three times more than that in Japanese textbooks. 2. In scientific inquiry category, Japanese science textbooks emphasized observing, predicting, measuring and problem cognition; Korean science textbooks emphasized experiment planning, observing and problem cognition. 3. In knowledge category, fact subcategory is mostly emphasized in both countries.

• Journal of Korea Game Society
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• v.15 no.4
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• pp.59-68
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• 2015

The progress in Information & Communication Technology enables data scientists to analyze big data for identifying peoples' daily lives and tacit preferences. A variety of industries already aware the potential usefulness of analyzing big data. However limited use of big data has been performed in game industry. In this research, we adopt data mining technique to analyze data gathered from a strategic simulation game. Decision Tree, Random Forest, Multi-class SVM, and Linear Regression techniques are used to find the most important variables to users' game levels. We provide practical guides for game design and usability based on the analyzed results.