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A Study on Heo Gyun's 'Clean(Cheong: 淸)' Kind Style Examined through Style Terminologies in Seongsushihwa(『惺叟詩話』) (『성수시화(惺叟詩話)』 속 풍격(風格) 용어(用語)를 통해 본 허균(許筠)의 '청(淸)'계열(系列) 풍격(風格) 연구(硏究) - 청경(淸勁)'·'청절(淸切)'·'청초(淸楚)'·'청월(淸越)'을 중심으로 -)

  • Yoon, Jaehwan
    • (The)Study of the Eastern Classic
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    • no.63
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    • pp.9-41
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    • 2016
  • This paper focuses on 'clean(cheong: 淸)' kinds of style terminologies among various style terminologies appearing in Heo Gyun's Seongsushihwa("惺?詩話") and tries to analyze the distinctive points which 'clean(cheong: 淸)' kinds of style terminologies include. In Heo Gyun's Seongsushihwa, 11 of 'clean' kinds of style terminologies, such as "cheonggyeong(淸勁), cheonghryang(淸亮), cheongryeo(淸麗), cheongseom(淸贍), cheongso(淸?), cheongweol(淸越), cheongjang(淸壯), cheongjeol(淸絶), cheongjeol(淸切), cheongchang(淸?), cheongcho(淸楚)," were used. This paper focuses and analyzes 'cheonggyeong(淸勁)', 'cheongjeol(淸切)', 'cheongcho(淸楚)', and 'cheongweol(淸越)' that he suggested through applying to real literary pieces. The result of analysis indicates that 'clean' kinds of style terminologies 'cheonggyeong', 'cheongjeol', 'cheongcho', and 'cheongweol' share the same 1st character 'clean(淸)', yet have distinctive qualities by the 2nd characters. These 4 style terminologies all share 'cheong(淸)' image which means clear and clean, yet each one has the attribute of the 2nd character that indicates each one's individual characteristic. It is apparent that 'Cheonggyeong(淸勁)' reflects the 'gyeong(勁)' image meaning upright and solid and implies poems of poets' steadfast spirit within clear boundary; 'cheongjeol(淸切)' reflects the 'jeol(切)' image meaning either desperation and imminence or pitifulness and sorrow and implies poems of poets' urgent and pitiful emotions within clear and clean boundary; 'cheongcho(淸楚)' reflects the 'cho(楚)' image meaning either delicacy and fineness or slenderness and tenderness and implies poems of poets' beautiful but not luxurious, delicate and tender emotions within clear and clean boundary; and 'cheongweol(淸越)' reflects the image of 'weol(越)' meaning unworldliness and excellency and implies poems, within clear and clean boundary, of excellent appearance and mentality surpassing mundane world. Compared with the 1st character's attributes of the style terminologies which Heo Gyun used, the 2nd characters's attributes do not appear that vivid. Especially, in the case that the 2nd characters have similar meanings, it is not easy to clarify the categories. Indeed, in order to grasp clear and distinctive qualities of style terminologies, the kinds of them need to be initially categorized by the 1st characters, and then sorted by the 2nd characters. In this case, the contents which the 2nd characters of style terminologies indicate should be considered. It is because style terminologies explain both literary pieces' aesthetic qualities and writers' personalities, and because explanations about literary pieces' aesthetic qualities includes not only the conclusive poetic or semantic boundaries which literary pieces' created but also literary pieces' creation processes and expression techniques. Through the style terminologies with Heo Gyun used in Seongsushihwa, it can be aware that he evaluated poems focussing more on the conclusive semantic boundaries that poets' spirits and poems created than expression techniques or creation methods. The overall aspects Heo Gyun's such style criticism has will be checked out in more detail through further studies by examining more materials.

A Methodology of Customer Churn Prediction based on Two-Dimensional Loyalty Segmentation (이차원 고객충성도 세그먼트 기반의 고객이탈예측 방법론)

  • Kim, Hyung Su;Hong, Seung Woo
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.111-126
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    • 2020
  • Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.