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Density-based spatial clustering of applications with noise using Gower distance
Jinkyung Yoo,Yujeong An,Young Min Kim 한국데이터정보과학회 2021 한국데이터정보과학회지 Vol.32 No.5
Most clustering algorithms considering spatial characteristics of data have been developed based on the geological location of observations. Density-based spatial clustering of applications with noise (DBSCAN) provides arbitrarily shaped clusters grouping a set of observations which are closely packed together and noise detecting outliers which lie alone in low-density regions. A distance measure for DBSCAN is Euclidean distance, which is the standard measure of distance and especially suitable to handle continuous variables. To handle both categorical and continuous variables simultaneously, other measures are required to compute distance for various types of variables. Thus, we propose DBSCAN algorithm using Gower distance. We provide numerical results on spatial and non-spatial setup comparing DBSCAN methods with Euclidean and Gower distance and we apply this method to land price data and migraine treatments data. DBSCAN using Gower distance has a reasonable method and gives comparably stable results.
소셜 미디어에서 사용자 행위 분석을 통한 사용자 평판 관리 기법
윤진경(Jinkyung Yun),정지원(Jiwon Jeong),이수지(Suji Lee),임종태(Jongtae Lim),복경수(Kyungsoo Bok),유재수(Jaesoo Yoo) 한국정보과학회 2016 정보과학회논문지 Vol.43 No.1
최근 소셜 네트워크 서비스는 단순히 사용자들의 인맥 관계 형성뿐만 아니라 다양한 형태의 정보를 생성하고 공유하는 개방형 플랫폼으로 변화하고 있다. 기존 사용자 평판 관리 기법은 사용자 프로필, 명시적 관계, 명시적 평가를 기반으로 사용자 신뢰성을 판별하기 때문에 명시적 평가가 잘 이루어지지 않는 소셜 미디어에서 사용자 신뢰성을 판별하기에는 부적합하다. 본 논문에서는 소셜 미디어에 대한 소셜행위들을 분석하고 명시적인 평가 뿐만 아니라 암시적 평가를 고려한 사용자 평판 관리 기법을 제안한다. 제안하는 기법은 소셜 행위로부터 긍정적 암시적 평가와 부정적 암시적 평가를 도출한다. 또한, 사용자의 전문성을 고려하기 위해 분야별로 사용자 평판 정보를 생성하고 사용자의 영향력을 판단하기 위해 평가에 참여한 사용자들의 수를 반영한다. 이를 통해 명시적 평가가 없는 사용자도 평판 정보를 생성할 수 있도록 하고 소셜 미디어에 더 적합한 사용자 평판 정보를 생성한다. Recently, social network services have changed by moving towards an open platform where, as well as simply allowing the building of relationships among users, various types of information can be generated and shared. Since existing user reputation management methods evaluate user reliability based on user profiles, explicit relations, and evaluation, they are not suitable for determining user reliability on social media due to few explicit evaluation. In this paper, we analyze social activities on social media and propose a new user reputation management method that considers implicit evaluation as well as explicit evaluation. The proposed method derives positive and negative implicit evaluation from social activities, and generates user reputation information by field in order to consider user expertise. It also considers the number of users that participate in evaluation in order to measure user influence. As a result, it generates the reputation information of users who have no explicit evaluation and creates user reputation information that is more suitable for social media.
User Reputation computation Method Based on Implicit Ratings on Social Media
( Kyoungsoo Bok ),( Jinkyung Yun ),( Yeonwoo Kim ),( Jongtae Lim ),( Jaesoo Yoo ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.3
Social network services have recently changed from environments for simply building connections among users to open platforms for generating and sharing various forms of information. Existing user reputation computation methods are inadequate for determining the trust in users on social media where explicit ratings are rare, because they determine the trust in users based on user profile, explicit relations, and explicit ratings. To solve this limitation of previous research, we propose a user reputation computation method suitable for the social media environment by incorporating implicit as well as explicit ratings. Reliable user reputation is estimated by identifying malicious information raters, modifying explicit ratings, and applying them to user reputation scores. The proposed method incorporates implicit ratings into user reputation estimation by differentiating positive and negative implicit ratings. Moreover, the method generates user reputation scores for individual categories to determine a given user`s expertise, and incorporates the number of users who participated in rating to determine a given user`s influence. This allows reputation scores to be generated also for users who have received no explicit ratings, and, thereby, is more suitable for social media. In addition, based on the user reputation scores, malicious information providers can be identified.
Relationship between Smartphone Use Time, Sitting Time, and Fitness Level in University Students
( Jun-il Yoo ),( Jinkyung Cho ),( Kyung-wan Baek ),( Min-ho Kim ),( Ji-seok Kim ) 한국운동생리학회(구 한국운동과학회) 2020 운동과학 Vol.29 No.2
PURPOSE: The purpose of this study was to investigate the relationship of smartphone use time, sitting time, and fitness level in university students. METHODS: The participants of this study were 237 healthy university students (157 males and 80 females) enrolled from 2017 to 2018. The smartphone use time was divided into three groups: 0-4, ≥4-10, and ≥10 hours, as was the sitting time: 0-4, 4-7, and ≥7 hours. Binary logistic regression was used to calculate the odd ratio (OR) and 95% confidence interval (CI) of the smartphone use time and sitting time for having low levels of fitness. RESULTS: There were linear decreases in physical fitness levels across incremental smartphone use time and sitting time. Compared to the smartphone use time for 0-4 hours (reference), smartphone use time for the ≥4-10 hours (OR=2.498; 95% CI=1.007-6.197; p=.048) or the 10≥group (OR=3.516; 95% CI=1.228-10.064; p=.019) had significantly higher ORs of having lower fitness even after adjustments for age, sex, physical activity and percent body fat. Likewise, logistic regression analyses showed that those who had sitting time for ≥7 hours (OR=3.135; 95% CI=1.155-8.512; p=.025) had significantly higher ORs of having lower fitness even after adjustments for age, sex, physical activity, and percent body fat, as compared with those who had sitting time for 0-<4 hours. CONCLUSIONS: The current finding suggest that the smartphone using time and sitting time were associated with having the risk of lower level of physical fitness in university students.