• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.164-165
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• 2021

In Korea, in the case of fire scenarios in performance design, it is assumed that the sprinkler is not working. In addition, it does not applicate various fire conditions. Therefore it is not enough that the accuracy about fire scenario. In foreign countries, reseach is being conducted to predict the casualities that can occur due to fire in the building space through statistical risk analysis. Also, research is consistently conducting for design that consider the sprinker probability of operation. Therefore, to analyze the fire risk of each building in Korea, the risk was analyzed using statistical data. As a result, the risk of casualties that can occur for each building use was analyzed as 0.6(persons/cases) for residential buildings, 0.25(persons/cases) for sales facilities, and 0.12(persons/cases) for buisiness facilities.

• Journal of the Korean Society of Food Culture
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• v.21 no.1
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• pp.51-56
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• 2006

This research suggests critical and specific decisive variables that affect general service quality of the products in family restaurants, considering features of services in which consumption and production happens simultaneous by collecting the raw data through point of sales. It also analyses the factors and helps to offer them practical strategies by providing managers of the restaurants and marketers with empirical viewpoints based on the research. Generally, family restaurants need their own physical environment and are required to encourage customers to revisit themselves by maintaining pleasant environment as well as, considering space for customers and for employees' working routes and effective maintenance of the facilities. The result of the study also tells that even if the unimportant factors did not affect much on the restaurants, management over these factors can be a differentiated strategy for competitive advantage over the other businesses.

• Journal of the Korean Applied Science and Technology
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• v.41 no.2
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• pp.436-446
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• 2024

This study contemplates the effect of social media advertising attributes on consumers' purchasing decisions in cosmetic products. It will serve as basic data for SNS's advertising strategy and marketing that promotes cosmetics sales. This study conducted a survey and went through the analysis process of SPSS v.25.0 statistical program. Frequency analysis, exploratory factor analysis, descriptive statistical analysis, correlation analysis, and regression analysis were conducted to analyze that informativity, reliability, entertainment, interactivity, and disturbance among SNS advertising attributes were used as effective advertising strategies, which had a positive effect on consumers' purchasing decisions. Therefore, in the cosmetics industry, it is necessary to seek strategies to actively utilize marketing in the social media space to promote consumers' purchase needs and to activate purchases.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.

• Korean Institute of Interior Design Journal
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• v.21 no.2
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• pp.174-185
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• 2012

The purpose of this study is to suggest planning strategy of tenant mix for UEC based on the final result of tenant mix analysis of five different research cases. The following is the comprehensive explanation about the result of tenant mix planning strategy for UEC currently in operation and when planning a new facility. First, overall research cases in this study show the tendency of following an old tradition, which stresses direct sales focusing on retail and dining adaptation. In order to compensate the defect, it is suggested to adopt new type of tenants with the functional mix of retail and dining with entertainment rather than decreasing the proportion of retail and dining tenant and increasing it of entertainment tenant. Second, the floorplan of UEC should adopt racetrack or circuit form that can stimulate shoppers' circular movement so to expose them to as much tenants as possible. Service consumption mode related tenants are required to place on the side or the edge of UEC, while retail consumption mode related tenants should be planned in the center. Among dining consumption mode related tenants, impulse dining tenants like a coffee shop should be placed at the turning point or at the end of the pathway, destination tenants like a restaurant and a food court, on the other hand, is needed to be placed in the center of the space. In case of Entertainment related tenants, destination tenants like bookstore or multiplex should also be placed at the end of the pathway, and on the way to those tenants, it is required to place general tenants that can share target customers with them. On the contrary, game center or record shop like tenants that can stimulate impulse sales should be placed on the visitor's main move or near the other destination tenants. Third, anchor tenants play an important role in gathering people to the UEC, and then induce them to visit the other tenants that are located near the anchors. Thus it is suggested to plan to place general tenants on the same floor as anchor tenants are placed so they can share the characteristics of target customers which create synergy effect.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.289-294
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• 2014

In the recent enterprises and are utilizing the CRM using data mining techniques and new marketing plan. However, data mining techniques are necessary expertise, general public access is difficult, it will be subject to constraints of time and space. in this paper, in order to solve this problem, we have proposed a Mobile CRM applying the data mining method. Thus, to analyze the structure of an existing CRM system, and defines the data flow and format. Also, define the process of the system, was designed sales trend analysis algorithm and customer sales recommendation algorithm using data mining techniques. Evaluation of the proposed system, through the test scenario to ensure proper operation, it was carried out the comparison and verification with the existing system. Results of the test, the value of existing programs and data matches to verify the reliability and use queries the proposed statistical tables to reduce the analysis time of data, it was verified rapidity.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.81-91
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• 2012

The world has been replacing fast fossil fuels vehicles with electric vehicles(EVs) to cope with climate change. The government set a goal which EVs will be substitute at least 10% of the domestic small vehicles with EVs until 2020, and will try to build electric charging infrastructures in apartments with the revision the law of 'the housing construction standards'. In apartments the EVs charging infrastructure and parking space is, essential to accomplish the goal. But the studies on EVs demand are few. In this study, we predicted that the demand for EVs using time-series analysis of statistical data, survey results for apartments residents in the metropolitan area. As a result, the ratio of the EVs appeared to be 6~21% for the total vehicles in a rental apartments for the years 2020, 21~39% in apartments for sales. For the EVs, the maximum power required for 1,000 households in rental apartment is predicted to be about 4200 kwh on a daily basis, while the maximum power in the apartment for sales is predicted to be 7800kwh.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1253-1262
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• 2014

Regression tree is a tree-structured solution in which a simple regression model is fitted to the data in each node made by recursive partitioning of predictor space. There have been many efforts to apply tree algorithms to various regression problems like logistic regression and quantile regression. Recently, algorithms have been expanded to the panel data analysis such as RE-EM algorithm by Sela and Simonoff (2012), and extension of GUIDE by Loh and Zheng (2013). The algorithms are briefly introduced and prediction accuracy of three methods are compared in this paper. In general, RE-EM shows good prediction accuracy with least MSE's in the simulation study. A RE-EM tree fitted to business survey index (BSI) panel data shows that sales BSI is the main factor which affects business entrepreneurs' economic sentiment. The economic sentiment BSI of non-manufacturing industries is higher than that of manufacturing ones among the relatively high sales group.

• Journal of Distribution Science
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• v.15 no.3
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• pp.73-80
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• 2017

Purpose - Since Korean distribution market was opened, the domestic environment in department stores has been changed by the pattern of consumption and consumer need based on income classes. As multilateral Free Trade Agreement (FTA) accelerates opening markets, the scale of circulating capital has become bigger. Large-scale commercial facilities have developed quickly as a form of a large shopping center, thus, the matter of choice and securing market area became an important valuable in this trend. Moreover, multi-complex space has been proposed as the goal of successful business with promoting the public benefit. Research design, data, and methodology - This research studied consumer behavior using data about the life style and sales of consumers, not statistical data or survey as previous studies. This research tried to find the differentiation in complex cultural space with consumption behavior of department store. Results - As the structure of society and culture was getting diverse and complex, economic growth and development with such diversity and complexity improved consumers' quality of life. The changes of consumer life style are quite natural like human instinct. Department stores have activated retail business with the products of accumulated technology. Moreover, they have created the space of consumption and culture. Because of these social and environmental changes, department stores are being developed as Multi-functional spaces as well as sale places considering the strategies of department and the changes of consumers' purchasing behaviors. Conclusions - Urban culture complex is a landmark standing for the culture era of 21st century. It has provided an opportunity for consumers to enjoy culture, and has been an important factor to improve company images. Based on these roles and needs, expectancy effects are related with consumer preference and space preference, and the attitude toward companies. Moreover, the expectancy effects from those relationships are getting bigger and bigger. We should respect nature, a characteristic of Korean architecture, maintain visual continuity that harmonies with nature in the development of the complex space of the domestic department stores, and should take significance in the development of the complex cultural space in the direction of feeling the hierarchy of the space to obtain the visual pleasure with the artificial structure.