S-Space College of Natural Sciences (자연과학대학) Dept. of Statistics (통계학과) Theses (Master's Degree_통계학과)
Multidimensional Scaling with Application to Twitter Network Data
거리행렬을 이용한 다차원 척도법과 Twitter 네트워크 자료에의 응용
- 자연과학대학 통계학과
- Issue Date
- 서울대학교 대학원
- distance measures; dissimilarity matrix; multidimensional scaling; social network; Twitter network data
- 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2013. 2. 임요한.
- Multidimensional scaling is applied in varied fields such as marketing, genetics, ecology, molecular biology, psychology and social networks. In majority, multidimensional scaling has its main purpose to verify the relationship between individuals by embedding high-dimensional observations on a sphere to points on a lower-dimensional sphere. Simply put, multidimensional scaling makes it possible to look through the large data by illustrating them with a simple plot. In the process of applying multidimensional scaling to the data, we need to define a dissimilarity matrix, which reflects the distance between the each pair of the entities. Under the certain restrictions, there can be a variety of distance measures to construct the dissimilarity matrix. In this paper, we introduce several different distance measures possibly used for multidimensional scaling and categorize those measures so that they can be used in an appropriate circumstance. An application to the actual data has been done with the network data from Twitter. By implementing different types of measures to the specific data, we would like to show the importance of selecting an appropriate distance measure for the data.