자료유형 | 학위논문 |
---|---|
서명/저자사항 | Algorithms for Analyzing Spatio-temporal Data. |
개인저자 | Nath, Abhinandan. |
단체저자명 | Duke University. Computer Science. |
발행사항 | [S.l.]: Duke University., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 170 p. |
기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
ISBN | 9780438377257 |
학위논문주기 | Thesis (Ph.D.)--Duke University, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Pankaj K. Agarwal. |
요약 | In today's age, huge data sets are becoming ubiquitous. In addition to their size, most of these data sets are often noisy, have outliers, and are incomplete. Hence, analyzing such data is challenging. We look at applying geometric techniques to |
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요약 | We then look at comparing real-valued functions, by computing a distance function between their merge trees (a small-sized descriptor that succinctly captures the sublevel sets of a function). Merge trees are robust to noise in the data, and can |
요약 | Finally we look at the problem of capturing shared portions between large number of input trajectories. We formulate it as a subtrajectory clustering problem - the clustering of subsequences of trajectories. We propose a new model for clustering |
일반주제명 | Computer science. |
언어 | 영어 |
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: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |