• Asia pacific journal of information systems
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• 제18권2호
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.

• Journal of the Korean Data and Information Science Society
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• 제18권1호
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• pp.185-194
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• 2007

The information of covariates are available to do fully efficient fractional imputation(FEFI). The new method, FEFI with logistic regression is proposed to construct complete contingency tables. Jackknife method is used to get a standard errors of log-odds ratio from the completed table by the new method. Simulation results, when covariates have more information about categorical variables, reveal that the new method provides more efficient estimates of log-odds ratio than either multiple imputation(MI) based on data augmentation or complete case analysis.

• 한국의상디자인학회지
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• 제13권1호
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• pp.125-134
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• 2011

The research is a survey and analysis of female apparel preferences, and recognition of the KS sizing system for adult female garments. The practical surveys in this research are examined by total number of subjects, 200 women who are living in Seoul, South Korea. The homogeneity test using Chi-square statistics, and the analysis of frequencies and ratios of contingency tables were performed with the data which are classified by age, education level, income level and housing modalities. The findings in this study are as follows: 1. Women's preferences for purchasing female garments indicate significant differences between subjects, such as age, education level, income level and housing modalities. Moreover, the following five types of adult female garments were analyzed in this study: upper garment, lower garment, one-piece apparel, sportswear and sleepwear. The results of the preference study show an indirect understanding of the KSK 0051 classification system for subjects of the survey. Therefore the preference study can be used as a pilot study for the sizing recognition survey. 2. Women's recognition of the KSK 0051 sizing system for adult female adult garments do not indicate significant differences based on the characteristics of age, education level, income level and housing modalities. The low recognition of the KS sizing system is due to too many details and complex numbers of application for users. Therefore, the sizing system should be simplified and rearranged to be more effective and have more recognizable categories.

• 한국방재학회 논문집
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• 제9권2호
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• pp.77-85
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• 2009

우리나라는 기후의 계절적 변화가 뚜렷하며 지역별 시간에 따른 강우발생의 특성이 다양하다. 이러한 계절적, 지역적 강우특성의 반영은 수공구조물의 설계 및 안정성 평가 시 매우 중요하다. 이때 설계 강우량의 선정을 위해 적절한 강우 지속시간, 강우량, 그리고 시간에 따른 강우양상을 결정해야 한다. 일반적으로 수공구조물의 설계 및 신뢰도 평가 시 설계강우에 대한 시간적 강우양상의 결정은 매우 중요하다. 본 연구에서는 강우사상을 분리하여 각 강우사상의 무차원화를 실시하였고 이를 4가지 양상으로 구분하여 감천유역의 시간에 따른 강우발생에 영향을 주는 인자를 규명하고자 하였다. 이 분석은 강우관측소의 지리학적 위치, 강우량, 강우 지속시간, 계절, 태풍 및 장마, 건 우기에 관련된 시간에 따른 강우양상의 발생빈도의 상관관계를 통한 분할표에 의한 군집분석을 통해 실시되었다. 본 연구를 통해 해당 지역에 대한 시간에 따른 강우양상 발생의 영향인자를 파악할 수 있으며 이는 결국 수공구조물의 설계 및 평가뿐 만 아니라 유역의 홍수대책수립 시 매우 중요한 사전자료로 활용될 수 있다.

• 대한원격탐사학회지
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• 제15권2호
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• pp.91-105
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• 1999

최근 다중 지질정보의 통합적 해석은 GIS의 중요한 응용 분야중 하나로 인식되고 있다. 공간통합을 위하여 지구통계학적 방법들이 개발되어 있지만, 통합결과와 입력 주제도들 사이의 관계에 대한 통계적, 정량적 분석방법론의 개발은 아직까지 체계적으로 정립되어 있지 못한 상황이다. 본 연구에서는 지질도, 지화학자료, 항공지구물리자료, 지형자료 및 원격탐사 영상등 다양한 지질정보등이 보고된 옥동지역을 대상으로 하여 광물 부존 예상도 작성 사례연구를 수행하여 기존에 이용되고 있는 여러 공간 통합 방법중 확실인자 (Certainty Factor: CF) 추정방법과 다변량 통계 분석방법중 하나인 주성분분석을 시험적인 통합방법으로 우선적으로 적용한 뒤, 입력 자료와 통합결과에 대한 정량적인 통계량 정보를 추출하고자 하였다. 입력 주제도와 통합 결과사이의 관계 규명에는 통계 분할표를 이용한 통계처리를 편의 분석에는 잭나이프 방법을 적용하였다. 통합정보에 대한 통계량 분석을 통하여, 통합 결과와 입력자료 사이의 정량적 관계를 추출할 수 있었으며, 부가적으로 입력자료의 상태수준에 대한 판단정보를 얻을 수 있었다. 이러한 결과는 GIS 관점에서 통합결과 해석에 중요한 결정보조자료로 활용될 수 있으며, 복잡한 다중정보를 다루는데 공간 통합문제에서도 입력정보 검증을 위한 일반적일 처리과정으로도 발전할 수 있을 것으로 생각된다.

• 산업공학
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• 제14권2호
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• pp.198-204
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• 2001

The ISO 9000 family of international quality management standards were revised on 15 December 2000. The ISO 9001:2000 standard is used for certification/registration and contractual purposes by organizations seeking recognition of their quality management system(QMS). We summarize key contents changed in ISO 9000:2000 family standards. To comply with ISO 9001:2000, we analyze the current QMS for steel outfit industry, using audit results from ISO 9001:1994 for seven steel outfit firms during last three years. We investigate statistical relationships between ISO 9001:2000 and ISO 9001:1994 requirements from a three dimensional contingency table with audit results. We observe that the importance of requirements of the ISO 9001:2000 sections makes a difference between companies.

• The Korean Journal of Ecology
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• 제9권3호
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• pp.135-147
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• 1986

The simplification and the searching trends of complex data which assumed relationship between predictor variables and object variables are one of primary objective of ecological research. This study was aimed to apply cononical analysis consisting of canonical correlation analysis and canonical variate analysis related to lichen vegetation and several environmental variables which are elevation, height on grond, exposure side and cover values. Data collected from the Duckyoo National Park in August 1985. Lichen species was ranked by eqivocation information theory with cover values. Canonical correlation analysis was applied to one data set both set both environmental variables and lichem family. In order to make two sets of data matrix the scale of position vector ordination was calculated from the vector scalar product for lichen species. Canonical variate analysis was applied to rearranged data which was made by interval class code for environmental variables. The sharpness values was calculated in frequency of cotingency tables and the dispersion profiles of each species in classes of environmental variables was designed to extract component values based on the decomposition of expected frequencies in contingency table. The results of canonical correlation analysis revealed canonical first correlation value 0.815(89%), and second correlation value 0.083(11%). Significance test showed that the hypothesis of joint mutuallity of canonical correlation is accepted (P>0.05). The relation between canonical score of vegetation variables and that of environmental variable indicated linear tendency.

• 대한수학교육학회지:학교수학
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• 제19권3호
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• pp.461-480
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• 2017

이 연구는 상관관계에 대한 교사 지식의 특징을 $2{\times}2$ 분할표를 활용하여 분석함으로써 상관관계 및 관련 개념 지도에 대한 교수학적 함의를 모색하고자 하였다. 이를 위해 $2{\times}2$ 분할표를 활용하여 상관관계에 대한 교사 지식의 특징을 알아보기 위한 지필검사 문항을 개발하였다. 지필검사 문항 개발에는 $2{\times}2$ 분할표와 관련된 선행 연구 검토를 통해 추출한 교수학적 이슈를 문항 개발의 주요 관점으로 구체화하여 반영하였다. 개발한 검사 문항을 활용하여 현직 중 고등학교 교사 53명을 대상으로 지필검사를 실시하고, 지필검사에 대한 교사들의 답변은 검사 문항 개발의 주요 관점에 비추어 분석하였다. 이러한 분석 과정을 통해 $2{\times}2$ 분할표로 주어진 변량 사이의 상관관계에 대한 교사 지식의 특징을 '내용 지식', '학생들의 이해에 대한 지식', '수업 활용 지식'의 3가지 측면에서 분석함으로써 학교 교육과정에서 상관관계 및 관련 개념을 다루는 것과 관련된 시사점을 설명하였다.

• 한국지구과학회지
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• 제25권1호
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• pp.10-21
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• 2004

Recently, spatial data integration for geoscientific application has been regarded as an important task of various geoscientific applications of GIS. Although much research has been reported in the literature, quantitative assessment of the spatial interrelationship between input data layers and an integrated layer has not been considered fully and is in the development stage. Regarding this matter, we propose here, methodologies that account for the spatial interrelationship and spatial patterns in the spatial integration task, namely a multi-buffer zone analysis and a statistical analysis based on a contingency table. The main part of our work, the multi-buffer zone analysis, was addressed and applied to reveal the spatial pattern around geological source primitives and statistical analysis was performed to extract information for the assessment of an integrated layer. Mineral potential mapping using multi-source geoscience data sets from Ogdong in Korea was applied to illustrate application of this methodology.

• 한국환경과학회지
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• 제22권8호
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.