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

Temporal data mining methodologies i...

Li, Dan.

FindBook

Google Book

Amazon

博客來

Temporal data mining methodologies in a geo-spatial decision support system.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Temporal data mining methodologies in a geo-spatial decision support system./
作者:	Li, Dan.
面頁冊數:	122 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3233.
Contained By:	Dissertation Abstracts International66-06B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3180804
ISBN:	0542210975

Temporal data mining methodologies in a geo-spatial decision support system.
Li, Dan.

Temporal data mining methodologies in a geo-spatial decision support system. - 122 p.

Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3233.

Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2005.

In this dissertation, temporal data mining methodologies are developed to facilitate knowledge discovery in the framework of a distributed Geo-spatial Decision Support System (GDSS), with a focus on drought risk management. In this process, climatic data are collected from a variety of sources at weather stations. However, there are two kinds of missing (or incomplete) data. First, data are partially missing because of temporary malfunction or unavailability of equipment. Imputation methods based on clustering and soft computing techniques are developed to solve this missing data problem. Second, some locations do not have local observed data due to cost, physical, or technical considerations. To generate association rules for these un-sampled locations, three spatial interpolation models are developed and integrated into the temporal data mining process.

ISBN: 0542210975Subjects--Topical Terms:

626642
Computer Science.

Temporal data mining methodologies in a geo-spatial decision support system.
LDR:03118nmm 2200301 4500 001 1814172
005 20060511113503.5
008 130610s2005 eng d
020 $a 0542210975
035 $a (UnM)AAI3180804
035 $a AAI3180804
040 $a UnM $c UnM
100 1 $a Li, Dan. $3 1279371
245 1 0 $a Temporal data mining methodologies in a geo-spatial decision support system.
300 $a 122 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 3233.
500 $a Supervisor: Jitender S. Deogun.
502 $a Thesis (Ph.D.)--The University of Nebraska - Lincoln, 2005.
520 $a In this dissertation, temporal data mining methodologies are developed to facilitate knowledge discovery in the framework of a distributed Geo-spatial Decision Support System (GDSS), with a focus on drought risk management. In this process, climatic data are collected from a variety of sources at weather stations. However, there are two kinds of missing (or incomplete) data. First, data are partially missing because of temporary malfunction or unavailability of equipment. Imputation methods based on clustering and soft computing techniques are developed to solve this missing data problem. Second, some locations do not have local observed data due to cost, physical, or technical considerations. To generate association rules for these un-sampled locations, three spatial interpolation models are developed and integrated into the temporal data mining process.
520 $a After data preparation and preprocessing, we look more closely at the temporal property of time series data. Because a periodic pattern indicates something persistent and predictable, it is important to identify and characterize the periodicity. In this dissertation, an approach for mining partial periodic association rules in temporal databases is discussed. This approach allows the discovery of periodic episodes such that the events in an episode are not constrained by a fixed order. Moreover, this approach treats the antecedent and consequent of a rule separately and allows time lag between them. Thus, rules discovered are useful for prediction.
520 $a Additionally, droughts occur infrequently by nature. To facilitate drought risk management, it is important to discover infrequent episodes from multiple data sequences. In this dissertation, an algorithm is developed for the discovery of infrequent episodes with a combination of bottom-up and top-down scanning schema. The information sharing between bottom-up and top-down scanning helps prune candidate episodes, and thus, efficiently find infrequent episodes that are interesting to users.
520 $a Overall, the objective of this research is to enhance the body of work in the area of temporal data mining to enable knowledge discovery in the context of a GDSS.
590 $a School code: 0138.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 0 $a The University of Nebraska - Lincoln. $3 1024939
773 0 $t Dissertation Abstracts International $g 66-06B.
790 1 0 $a Deogun, Jitender S., $e advisor
790 $a 0138
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3180804