東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Unsupervised methods to discover eve...

Chen, Xi.

FindBook

Google Book

Amazon

博客來

Unsupervised methods to discover events from spatio-temporal data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Unsupervised methods to discover events from spatio-temporal data./
作者:	Chen, Xi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	123 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10283354
ISBN:	9780355090499

Unsupervised methods to discover events from spatio-temporal data.
Chen, Xi.

Unsupervised methods to discover events from spatio-temporal data. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 123 p.

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--University of Minnesota, 2017.

Unsupervised event detection in spatio-temporal data aims to autonomously identify when and/or where events occurred with little or no human supervision. It is an active field of research with notable applications in social, Earth, and medical sciences. While event detection has enjoyed tremendous success in many domains, it is still a challenging problem due to the vastness of data points, presence of noise and missing values, the heterogeneous nature of spatio-temporal signals, and the large variety of event types.

ISBN: 9780355090499Subjects--Topical Terms:

523869
Computer science.

Unsupervised methods to discover events from spatio-temporal data.
LDR:03813nmm a2200325 4500 001 2126940
005 20171128112458.5
008 180830s2017 ||||||||||||||||| ||eng d
020 $a 9780355090499
035 $a (MiAaPQ)AAI10283354
035 $a AAI10283354
040 $a MiAaPQ $c MiAaPQ
100 1 $a Chen, Xi. $3 1017731
245 1 0 $a Unsupervised methods to discover events from spatio-temporal data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 123 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Adviser: Vipin Kumar.
502 $a Thesis (Ph.D.)--University of Minnesota, 2017.
520 $a Unsupervised event detection in spatio-temporal data aims to autonomously identify when and/or where events occurred with little or no human supervision. It is an active field of research with notable applications in social, Earth, and medical sciences. While event detection has enjoyed tremendous success in many domains, it is still a challenging problem due to the vastness of data points, presence of noise and missing values, the heterogeneous nature of spatio-temporal signals, and the large variety of event types.
520 $a Unsupervised event detection is a broad and yet open research area. Instead of exploring every aspect in this area, this dissertation focuses on four novel algorithms that covers two types of important events in spatio-temporal data: change-points and moving regions.
520 $a The first algorithm in this dissertation is the Persistence-Consistency (PC) framework. It is a general framework that can increase the robustness of change-point detection algorithms to noise and outliers. The major advantage of the PC framework is that it can work with most modeling-based change-point detection algorithms and improve their performance without modifying the selected change-point detection algorithm. We use two real-world applications, forest fire detection using a satellite dataset and activity segmentation from a mobile health dataset, to test the effectiveness of this framework.
520 $a The second and third algorithms in this dissertation are proposed to detect a novel type of change point, which is named as contextual change points. While most existing change points more or less indicate that the time series is different from what it was before, a contextual change point typically suggests an event that causes the relationship of several time series changes. Each of these two algorithms introduces one type of contextual change point and also presents an algorithm to detect the corresponding type of change point. We demonstrate the unique capabilities of these approaches with two applications: event detection in stock market data and forest fire detection using remote sensing data.
520 $a The final algorithm in this dissertation is a clustering method that discovers a particular type of moving regions (or dynamic spatio-temporal patterns) in noisy, incomplete, and heterogeneous data. This task faces two major challenges: First, the regions (or clusters) are dynamic and may change in size, shape, and statistical properties over time. Second, numerous spatio-temporal data are incomplete, noisy, heterogeneous, and highly variable (over space and time). Our proposed approach fully utilizes the spatial contiguity and temporal similarity in the spatio-temporal data and, hence, can address the above two challenges. We demonstrate the performance of the proposed method on a real-world application of monitoring in-land water bodies on a global scale.
590 $a School code: 0130.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a University of Minnesota. $b Computer Science. $3 1018528
773 0 $t Dissertation Abstracts International $g 78-12B(E).
790 $a 0130
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10283354