• 지능정보연구
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• 제8권2호
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• pp.15-35
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• 2002

최근 데이터 마이닝 기법이 주목받고 있는 이유 중의 가장 큰 이유는 자사가 보유하고 있는 고객의 특성을 파악함으로써 기존의 고객을 효과적으로 유지ㆍ관리할 수 있도록 지원하기 때문이다. 특히 고객 보유율 5%신장이 수익률 120% 증대를 가져오는 것으로 보고되고 있는 신용카드 업계에서는 신규고객을 확보하는 것만큼 기존 고객을 유지ㆍ관리하는 것이 중요하다. 특히, 신용카드를 발급 받고 거의 사용하지 않은 고객이나 쉽게 이탈하는 고객을 판별하는 것은 신용카드사의 입장에서는 비용절감 차원에서 매우 중요하다. 그러나 아직까지 어떠한 속성을 보유하고 있는 고객이 쉽게 이탈하는지를 판별할 수 있는 연구는 거의 진행되지 않았다. 이에 본 연구에서는 데이터 마이닝 기법 중 널리 알려진 인공신경망, 로지스틱 회귀분석, C5.0방법을 이용하여 신용카드 시장에서의 고객현황에 대하여 분석하고자 한다. 이를 위하여 본 연구에서는 모 신용카드사의 최근 4년간(97년 3월 이후) 가입고객 및 이탈고객을 대상으로 실증분석을 실시하였다 분석결과 신용카드 시장에서 카드를 지속적으로 보유하고 있는 고객과 이탈하는 고객을 구분하는 속성이 존재함을 발견하였고, 이를 바탕으로 신용카드사가 수립해야 할 마케팅 전략을 제시하였다.

• 패션비즈니스
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• 제23권3호
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• pp.85-100
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• 2019

This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand's image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, "fashion shows," was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in "models" was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.

• 지식경영연구
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• 제20권1호
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• pp.45-75
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• 2019

Recently, the growth of beauty industry such as plastic surgery and beauty is continued every year in Korea. With the increased interest in appearance based on the improvement of life standard and the development of media, people's perception of cosmetic plastic surgery is changing. Now, as the service for consumer satisfaction based on their desire, the perception of plastic surgery medical service is changed to the high value-added industry with the high growth potential. Thus, this study aims to suggest the strategies for providing the medical service that could satisfy customers, by drawing the factors cognized as important when customers aim to get the cosmetic plastic surgery, and then additionally analyzing the relationships of those factors. On top of performing the topic modeling based on customers' comments data of social commerce related to cosmetic plastic surgery, this study also conducted the network analysis for visualizing the relations of each keywords. The drawn main factors were divided by applying the sub-categories of the SERVQUAL theory, and the additional characteristics of plastic surgery were shown by referring the relevant previous researches. Moreover, the interview with the cosmetic plastic surgery specialists (plastic surgeons) and customers who actually received the plastic surgery, helped the understanding of the interpretation of each factor and the actual relevant phenomenons. The significance of this study is to draw and discuss the main factors that should be observed by Korean cosmetic plastic surgery medical institutes, by mining and analyzing the opinions of customers interested in the cosmetic plastic surgery and procedure with the use of topic modeling. In other words, the quality of medical service of cosmetic plastic surgery could be improved by presenting the key factors that could be considered by the cosmetic plastic surgery medical service suppliers and also the actual strategies.

• 디지털융복합연구
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• 제20권3호
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• pp.131-143
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• 2022

본 연구는 핀테크를 중심으로 국내 디지털 금융 서비스 시장의 트렌드를 파악하고자 신문기사와 트위터 데이터를 대상으로 텍스트마이닝 기법을 사용하여 분석을 진행하였다. 핀테크 시장의 성장 과정에 있어서 간편결제 서비스 도입, 인터넷전문은행 출범, 데이터 3법 개정안 통과, 마이데이터 사업 신청 등 중요하게 작용을 한 4가지 시점을 기준으로 빈도분석을 수행하여 핵심 키워드 간의 차이를 살펴보았다. 또한 핀테크 선도 국가인 중국·미국과 미래 키워드를 핀테크 키워드와 결합한 빈도분석 결과를 통해 세계 시장 속에서 국내 핀테크 산업의 현 위치와 미래 시장 전망을 예측하였다. 마지막으로 트위터 트윗을 대상으로 감성분석을 진행하여 핀테크 서비스에 대한 소비자의 기대와 우려를 정량화하였다. 따라서 본 연구는 금융 생태계 변화 과정을 살펴보고, 분석 결과를 종합함으로써 정부와 기업이 향후 핀테크 시장 발전에 있어서 활용할 수 있는 전략적 방향성 및 대응 전략을 제시한 점에서 의의가 있다.

• 정보처리학회논문지B
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• 제16B권5호
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• pp.411-426
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• 2009

대부분의 메타휴리스틱들은 동물의 행동을 모방한 것이다. 본 논문에서는 Mine 알고리즘을 제안한다. Mine 알고리즘(Mine Algorithm)은 인간의 행동을 모방한 메타휴리스틱이다. 탐색의 관점에서 인간의 노하우와 휴리스틱이 가장 잘 녹아 있는 업종은 광산업(mining industry)이다. Mine 알고리즘에서는 광산 업무에 초점을 맞추어서 인간의 행동패턴을 형식화한다. Mine 알고리즘은 다양한 탐색기법을 유연하게 구사하며, 그 때문에 광범위한 문제에서 고른 성능을 보인다. 즉, 범용성이 양호하다. 우리는 기존 메타휴리스틱들과의 비교 실험을 통하여 Mine 알고리즘의 개선된 범용성을 보인다.

• 디지털산업정보학회논문지
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• 제9권2호
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• pp.139-149
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• 2013

As the importance of technology forecasting while countries and companies manage the R&D project is growing bigger, the methodology of technology forecasting has been diversified. One of the forecasting method is patent analysis. This research proposes quick forecasting process of emerging technology based on keyword approach using text mining. The forecasting process is following: First, the term-document matrix is extracted from patent documents by using text mining. Second, emerging technology keyword are extracted by analyzing the importance of word from utilizing mean values and standard deviation values of the term and the emerging trend of word discovered from time series information of the term. Next, association between terms is measured by using cosine similarity. finally, the keyword of emerging technology is selected in consequence of the synthesized result and we forecast the emerging technology according to the results. The technology forecasting process described in this paper can be applied to developing computerized technology forecasting system integrated with various results of other patent analysis for decision maker of company and country.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 공동추계학술대회
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• pp.509-515
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• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.

• 한국의류산업학회지
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• 제23권3호
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• pp.380-389
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• 2021

This study aimed to reveal the perceptions and trends of digital fashion technology through an informational approach. A big data analysis was conducted after collecting the text shown in a web environment from April 2019 to April 2021. Key words were derived through text mining analysis and network analysis, and the structure of perception of digital fashion technology was identified. Using textoms, we collected 8144 texts after data refinement, conducted a frequency of emergence and central component analysis, and visualized the results with word cloud and N-gram. The frequency of appearance also generated matrices with the top 70 words, and a structural equivalent analysis was performed. The results were presented with network visualizations and dendrograms. Fashion, digital, and technology were the most frequently mentioned topics, and the frequencies of platform, digital transformation, and start-ups were also high. Through clustering, four clusters of marketing were formed using fashion, digital technology, startups, and augmented reality/virtual reality technology. Future research on startups and smart factories with technologies based on stable platforms is needed. The results of this study contribute to increasing the fashion industry's knowledge on digital fashion technology and can be used as a foundational study for the development of research on related topics.

• Journal of Electrochemical Science and Technology
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• 제12권4호
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• pp.387-397
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• 2021

Deep eutectic solvents have been established as feasible metal electroplating solvent alternatives over traditional toxic aqueous plating baths. However, water, either added intentionally or unintentionally, can significantly influence the solvent's physical properties and performance, thereby hindering its industry application. In this study, the hygroscopicity, or the ability to absorb moisture from the environment, of synthesized ethaline (1:2 choline chloride:ethylene glycol) was investigated. The kinematic viscosity, electrical conductivity, electrochemical window, and water content of ethaline were monitored over a 2-week period. Karl Fischer titration tests showed that ethaline exposed to the atmosphere displayed significant hygroscopicity compared to its unexposed counterpart. ¹H NMR spectroscopy revealed that water vapor was readily absorbed at the surface due to the hydrophilic groups present in the ethaline molecule. Water uptake resulted in the decrease in viscosity, increase in electrical conductivity and narrowing of the electrochemical window of ethaline. Solution heating at 100℃ removed the absorbed moisture and allowed the recovery of the solvent's initial properties.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권8호
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• pp.2704-2719
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• 2022

Lately, neo-Chinese style furniture has been frequently noticed by product design professionals for the big part it played in promoting traditional Chinese culture. This article is an attempt to use big data semantic analysis method to provide effective design research method for neo-Chinese furniture design. By using big data mining program TEXTOM for big data collection and analysis, the data obtained from typical websites in a set time period will be sorted and analyzed. On the basis of "neo-Chinese furniture" samples, key data will be compared, classification analysis of overall data, and horizontal analysis of typical data will be performed by the methods of word frequency analysis, connection centrality analysis, and TF-IDF analysis. And we tried to summarize according to the related views and theories of the design. The research results show that the results of data analysis are close to the relevant definitions of design. The core high-frequency vocabulary obtained under data analysis, such as popular, furniture, modern, etc., can provide a reasonable and effective focus of attention for the designs. The result obtained through the systematic sorting and summary of the data can be a reliable guidance in the direction of our design. This research attempted to introduce related big data mining semantic analysis methods into the product design industry, to supply scientific and objective data and channels for studies on design, and to provide a case on the practical application of big data analysis in the industry.