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Clustering Consistently
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Clustering Consistently. |
| 개인저자 | Eldridge, Justin. |
| 단체저자명 | The Ohio State University. Computer Science and Engineering. |
| 발행사항 | [S.l.]: The Ohio State University., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 141 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438097896 |
| 학위논문주기 | Thesis (Ph.D.)--The Ohio State University, 2017. |
| 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 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
