DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Sooyong | - |
dc.contributor.advisor | 김수용 | - |
dc.contributor.author | Kim, Sehyun | - |
dc.contributor.author | 김세현 | - |
dc.date.accessioned | 2018-05-23T19:33:21Z | - |
dc.date.available | 2018-05-23T19:33:21Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675692&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/241765 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 물리학과, 2017.2,[vii, 74 p. :] | - |
dc.description.abstract | For complex systems described as networks, modularity maximization has been emerging as one of community detection methods like PCA and network analysis, due to their intuitive concept and application potential to real systems in spite of the resolution limit. By the traditional methods, however, sub-community structure may not be clearly revealed in many cases. For the complex system of which nodes are expressed in high-dimensional feature vectors, we propose a new procedure using archetypal analysis (AA) and an invented quality function for uncovering the multiscale community structure of the system, visualize the structure with t-SNE and also use machine learning techniques for optimization issues. In this thesis, we study macro- and sub-community structures of various complex systems including generated system, financial system and bioinformatics system with the proposed approach and other traditional methods, and also show that the proposed one can overcome the limit of traditional ones in community detection. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | community | - |
dc.subject | network | - |
dc.subject | modularity | - |
dc.subject | quality funciton | - |
dc.subject | archetypal analysis | - |
dc.subject | t-SNE | - |
dc.subject | PCA | - |
dc.subject | 커뮤니티 | - |
dc.subject | 네트워크 | - |
dc.subject | 모듈러리티 | - |
dc.subject | 품질함수 | - |
dc.subject | 원형분석 | - |
dc.title | New approach to sub-community structure in high-dimensional data | - |
dc.title.alternative | 고차원 데이터의 서브 커뮤니티에 대한 새로운 접근 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :물리학과, | - |
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