Until now, research on consumers’ purchasing behavior has primarily focused on psychological aspects or depended on consumer surveys. However, there may be a gap between consumers’ self-reported perceptions and their observable actions. In response, this study aimed to investigate consumer purchasing behavior utilizing a big data approach. To this end, this study investigated the purchasing patterns of fashion items, both online and in retail stores, from a data-driven perspective. We also investigated whether individual consumers switched between online websites and retail establishments for making purchases. Data on 516,474 purchases were obtained from fashion companies. We used association rule analysis and K-means clustering to identify purchase patterns that were influenced by customer loyalty. Furthermore, sequential pattern analysis was applied to investigate the usage patterns of online and offline channels by consumers. The results showed that high-loyalty consumers mainly purchased infrequently bought items in the brand line, as well as high-priced items, and that these purchase patterns were similar both online and in stores. In contrast, the low-loyalty group showed different purchasing behaviors for online versus in-store purchases. In physical environments, the low-loyalty consumers tended to purchase less popular or more expensive items from the brand line, whereas in online environments, their purchases centered around items with relatively high sales volumes. Finally, we found that both high and low loyalty groups exclusively used a single preferred channel, either online or in-store. The findings help companies better understand consumer purchase patterns and build future marketing strategies around items with high brand centrality.

We performed a study to examine the association between diet quality and nonalcoholic fatty liver disease (NAFLD). Our study included 3,586 women aged 40-64 years who participated in the sixth Korea National Health and Nutrition Examination Survey. The study subjects were classified into the NAFLD group (n=816) and the normal group (n=2,770) using the hepatic steatosis index. The anthropometric indices, blood profiles, and dietary intake data of the subjects were obtained. The waist circumference, body mass index, and the serum levels of triglycerides, fasting blood sugar, HbA1c, and systolic and diastolic blood pressures were higher in the NAFLD compared to the normal groups (p<0.001, respectively). The intakes of protein (g/kg body weight, p<0.001), potassium (p<0.001), and vitamin A (p=0.006) were significantly lower in the NAFLD group. It was observed that the higher the total Korean Healthy Eating Index score, the lower the risk of NAFLD. A reverse relationship was shown between the NAFLD risk and the intakes of total fruits, total vegetables, vegetables excluding Kimchi and pickled vegetables, meat, fish, eggs and beans. Therefore, it is recommended that middleaged women in Korea increase their intakes of fruits, vegetables, and foods high in protein for the proper management of NAFLD.

In this study, we aimed to explore whether eating alone is associated with mental health conditions in Korean adolescents. The data of 2,012 Korean adolescents aged 12-18 years were obtained from the Korea National Health and Nutrition Examination Survey 2015–2019. Participants were classified into three groups based on the frequency of eating alone: none (all meals with others); 1 meal/day alone; and 2 meals/day alone. Mental health conditions were assessed based on stress recognition, depressive symptoms, and suicidal ideation. Multivariable logistic regressions were employed to calculate the adjusted odds ratios (AORs) and 95% confidence intervals (CIs) of poor mental health conditions according to the frequency of eating alone. Adolescents who ate 2 meals/day alone had higher odds of stress recognition (AOR: 2.65, 95% CI: 1.94- 3.63), depressive symptoms (AOR: 2.55, 95% CI: 1.47-4.42), and suicidal ideation (AOR: 2.53, 95% CI: 1.05-6.08) than those who ate all their meals with others. In addition, having breakfast or dinner alone increased the odds of stress recognition. Considering the continuous increase in the social phenomenon of eating alone, nutritional educations are needed to develop adolescents' ability to choose more nutritionally balanced and healthy meals when eating alone.

The purpose of this study was to investigate the association between dietary macronutrient intakes and prevalence of metabolic syndrome (MetS) in Korean adults. Data were obtained from the Korean National Health and Nutrition Examination Survey (2013-2017), and a total of 11,600 Korean adults (4,918 men, 6,682 women) aged 50 years and older were analyzed. The daily intakes and percentages of energy from carbohydrates or fat in men or women with MetS were significantly lower than in normal subjects, respectively. High carbohydrate or fat percentages were negatively associated with MetS based on adjusted odds ratios (OR) of 0.804 in men (p=0.034) and 0.820 in women (p=0.045), respectively. A high percentage of energy from carbohydrates was positively associated with reduced waist circumference, diastolic blood pressure, triglyceride and high-density lipoprotein (HDL) cholesterol in men. On the other hand, a high percentage of energy from fat was positively related with elevated HDL cholesterol and reduced triglycerides in women. In conclusion, our study indicates that high carbohydrate or fat intake is associated with risk of MetS in Korean men or women aged 50 years and older, respectively. Further prospective studies are necessary to elucidate the association between macronutrient intakes and MetS among Korean adults according to age.

We study data mining technique in an electronic commerce. Customers travel web pages in an shopping mall and they sometimes purchase products. It is important for a web master in a shopping mall to know customer's purchasing patterns. We discover both association rules among customer's purchasing products and customer's traversal paths. We propose three phase mining technique to explore it. In the first phase, it find large items from sales database. In the second phase, it add to traversal paths. In the third phase, it discover associations rules from large items.

Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot’s orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot’s pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.