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Clustering Consistently
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Eldridge, Justin. |
| 단체저자명 | The Ohio State University. Computer Science and Engineering. |
| 서명/저자사항 | Clustering Consistently. |
| 발행사항 | [S.l.] : The Ohio State University., 2017 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2017 |
| 형태사항 | 141 p. |
| 소장본 주기 | School code: 0168. |
| ISBN | 9780438097896 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: Mikhail Belkin |
| 요약 | Clustering is the task of organizing data into natural groups, or clusters. A central goal in developing a theory of clustering is the derivation of correctness guarantees which ensure that clustering methods produce the right results. In this d |
| 요약 | In the first part, we study the setting in which data are drawn from a probability density supported on a subset of a Euclidean space. The natural cluster structure of the density is captured by the so-called high density cluster tree, which is |
| 요약 | We will show that Hartigan's notion of consistency is in fact not strong enough to ensure that an algorithm recovers the density cluster tree as we would intuitively expect. We identify the precise deficiency which allows this, and introduce a n |
| 요약 | In the sequel, we consider the clustering of graphs sampled from a very general, nonparametric random graph model called a graphon. Unlike in the density setting, clustering in the graphon model is not well-studied. We therefore rigorously analy |
| 일반주제명 | Artificial intelligence. Statistics. Computer science. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T15000287 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00028209 | 001.5 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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