DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 안정연 | - |
dc.contributor.author | An, Seongbin | - |
dc.contributor.author | 안성빈 | - |
dc.date.accessioned | 2024-07-30T19:31:02Z | - |
dc.date.available | 2024-07-30T19:31:02Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096687&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321470 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2024.2,[iii, 27 p. :] | - |
dc.description.abstract | Clustering complex data presents significant uncertainty, particularly in cluster interpretation. In many practical scenarios, it is often desired to interpret discovered clusters in an ordered fashion. For example, in healthcare, doctors aim to categorize patients into high-, medium-, and low-risk groups. To address this challenge, we introduce “monotone clustering”, a novel method that identifies inherently ordinal clusters from high-dimensional data. The essence of monotone clustering lies in ensuring that cluster labels are monotonically related to each input variable. We utilize a generalized additive model fortified with monotone splines. Recognizing that not all input variables might influence the ordinal clusters, we incorporate a sign-coherent sparse group penalty on the spline coefficients. This approach aids in highlighting crucial variables and eliminating noise or irrelevant ones. Our algorithm iteratively refines nonlinear monotone functions for the generalized additive model based on existing ordinal clusters and revises cluster assignments using model predictions. The effectiveness and superiority of our monotone clustering approach are substantiated through simulation studies and two real-world examples. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 순서 레이블▼a군집화▼a변수 선택▼a희소 일반화가법모형 | - |
dc.subject | Ordinal labels▼aClustering▼aVariable selection▼aSparse generalized additive model | - |
dc.title | Monotone clustering with sparse generalized additive model | - |
dc.title.alternative | 희소 일반화가법모형을 이용한 단조 군집화 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :산업및시스템공학과, | - |
dc.contributor.alternativeauthor | Ahn, Jeongyoun | - |
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