Earticle

The consequences of rapid industrial advancement, diversified types of business and unexpected industrial accidents have caused a lot of damage to many unspecified persons both in a human way and a material way Although various previous studies have been analyzed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. The main objective of this study is to find an optimal algorithm for data analysis of industrial accidents and this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and AnswerTree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work chosen from 19,574 data related to construction industries during three years (2002~2004) in Korea.

The main objective of the statistical analysis about industrial accidents is to find out what is the dangerous factor in its own industrial field so that it is possible to prevent or decrease the number of the possible accidents by educating those who work in the fields for safety tools. However, so far, there is no technique of quantitative evaluation on danger. Almost all previous researches as to industrial accidents have only relied on the frequency analysis such as the analysis of the constituent ratio on accidents. As an application of data mining technique, this paper presents analysis on the efficiency of the CHAID algorithm to classify types of industrial accidents data and thereby identifies potential weak points in accident risk grouping.

산업재해 통계분석의 커다란 목적은 각 산업별로 주 위험요인을 도출하고 이에 따른 안전교육의 실시 또는 안전장치 등을 보완함으로써 산업재해를 줄이거나 예방하는데 있다고 볼 수 있다. 그러나 일반 제조업이나 건설업 등에서는 아직까지도 정량적 위험성 평가 기법이 개발되어 있지 않은 실정이다. 따라서 효율적인 위험성 평가 기법의 개발이 필요하다. 본 연구에서는 데이터마이닝 기법을 이용한 산업재해 예방을 위한 최적 알고리즘 선정 방법을 제시한다.

Many researches have been focused on the analysis of industry disasters in order to reduce them. As a similar endeavor, this paper provides a propensity analysis of injured people from various industries using classification and regression tree(CART), a data mining algorithm. The sample for this work was chosen from 25,157data related to various industries during one year ( 2003.2~2004.1) at Kangwon-Do in Korea. For the purpose of this paper, eight independent variables (injured date, injured time, injured month, type of Injured person, continuous service period, sex, company size, age)are taken from injured person group. According to the analysis result, it is found that five out of the eight factors that are predicted as significant have salient effects. Factors of season, time/hour, day of the week, or month which disasters happened do not show any significant effect. This paper provides common features of injured people. The provided analysis result will be helpful as a starting point for root cause analysis and reduction of industry disasters and also for development of a guideline of safety management.

본 연구는 독립 커피전문점 산업의 규모가 성장하는 배경에 따라 독립 커피전문점 시장의 관점에서 시장세분화 결과를 제시하고자 하였으며, 독립 커피전문점을 로스터리와 비 로스터리 점포 유형으로구분, 소비자층은 인구통계학적 특성과 출생년도에 따른 세대구분에 따라 세분화하여 Z세대 소비자를 분류하였다. 분석을 위해 최근 6개월 이내에 커피전문점을 방문한 내국인 소비자를 대상으로 온라인 설문조사를실시하였으며, 307개 응답을 분석에 활용하였다. 자료의 분석은 SPSS 22.0 프로그램의 의사결정나무CHAID 알고리즘 분석을 활용하여 분석하였으며 분석의 결과는 다음과 같다. 첫 번째 분리마디는 Z세대의구분에 따라 분리되었으며, 이하 분리노드에서 교육수준, 성별, 연령 직업에 따라 유의한 시장세분화 결과를나타냈다. 분석의 오분류 확률은 0.290으로 의사결정나무 모델은 약 71%의 정확성을 내포하였다.

The purpose of this study is to present target consumers through market segmentation according to the growth of the independent coffee shop market. Accordingly, store types of independent coffee shops were classified, and market segmentation analysis was performed according to demographic characteristics and consumer generation. Independent coffee shops were subdivided according to whether they operate coffee roastery shops or not, and the consumer segment classified Generation Z consumers by subdividing them according to demographic characteristics and birth year. An online questionnaire survey was conducted on Koreans who visited independent coffee shops within the past six months for data analysis, and 307 samples were used for research. The data were analyzed through answertree CHAID algorithm in SPSS 22.0 program and the results of the analysis are as follows: According to the analysis, the first split node was divided into Generation Z. Also, the results of answertree showed that there were statically significant differences in variables (education level, gender, age, job) among groups. In addition, the risk estimate was 0.290, the answertree model implied 71% accuracy. The results of this study would be meaningful for independent coffee shop businesses to identify targeting customer segmentation so that they could make ideal marketing strategies.

The purposes of this study were (1) to classify family restaurant customers based on their selection preferences and (2) to identify the differences according to demographic characteristics and dining-out behaviors and types. First, this study conducted the principal component factor analysis with varimax rotation to extract the selection preferences. As a result, there were four factors; food, physical environment, employee service, and access. Second, cluster analysis showed three different groups: 'food-seeker,' 'employee service and access-seeker,' and 'physical environment-seeker.' Third, the results of AnswerTree showed that there were statically significant differences in three variables (expenditure, with whom customers go to a restaurant, and sex) among three groups. The results of this study would be meaningful for marketers or managers in the family restaurant business to identify targeting customer segmentation so that they could make ideal marketing strategies.

In the Fourth Industrial Revolution, drone food delivery services are attracting attention due to its great advantages. However, there is no existing research on market segmentation in the context of drone food delivery services. Therefore, the purpose of this research was to segment sub-groups based on five sub-dimensions of perceived benefits, and identify the differences in the sub-groups according to demographic factors using the Answertree method. A total of 720 samples were used for analysis. As a result of the analysis for each expected benefit type, the split node of Answertree was created according to convenience, emotion, and compatibility, and significant market segmentation results were confirmed according to gender, age, education level, and marital status for each model. The risk estimates for each model are .392, .418, and .428, which implies a model accuracy of 60.8%, 58.2%, and 57.2%.