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Spatial data mining model for satell...

Sabry, Ahmed Esmat.

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Spatial data mining model for satellite image classifier.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spatial data mining model for satellite image classifier./
作者:	Sabry, Ahmed Esmat.
面頁冊數:	62 p.
附註:	Source: Masters Abstracts International, Volume: 42-01, page: 0266.
Contained By:	Masters Abstracts International42-01.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1415201

Spatial data mining model for satellite image classifier.
Sabry, Ahmed Esmat.

Spatial data mining model for satellite image classifier. - 62 p.

Source: Masters Abstracts International, Volume: 42-01, page: 0266.

Thesis (M.S.)--University of Louisville, 2003.

Spatial data mining, mining knowledge from large amounts of spatial data, is a highly demanding field. Huge amounts of spatial data have been collected in various applications, i.e. remote sensing, geographical information systems, environmental assessment and planning. The collected data exceeded human ability to analyze. This thesis shows a proposed data-mining model for mining a spatial data acquitted from a remote sensed satellite images.Subjects--Topical Terms:

626642
Computer Science.

Spatial data mining model for satellite image classifier.
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100 1 $a Sabry, Ahmed Esmat. $3 1947605
245 1 0 $a Spatial data mining model for satellite image classifier.
300 $a 62 p.
500 $a Source: Masters Abstracts International, Volume: 42-01, page: 0266.
500 $a Adviser: Adel S. Elmaghraby.
502 $a Thesis (M.S.)--University of Louisville, 2003.
520 $a Spatial data mining, mining knowledge from large amounts of spatial data, is a highly demanding field. Huge amounts of spatial data have been collected in various applications, i.e. remote sensing, geographical information systems, environmental assessment and planning. The collected data exceeded human ability to analyze. This thesis shows a proposed data-mining model for mining a spatial data acquitted from a remote sensed satellite images.
520 $a The thesis is divided into six chapters. Chapter one gives an overview of spatial data mining concept and includes problem definition and research objectives. Chapter two explores the theoretical framework of spatial data mining and explains the spatial data-mining phases. Chapter three centers on an unsupervised classification technique, as an essential part of the proposed spatial data-mining model. Chapter four illustrates the proposed model with used algorithms explanation, complexity and implementation. Chapter five shows the selected dataset, applied experiments, and the results.
520 $a Finally, chapter six is the conclusion, which includes summary of work, future recommendation and limitations.
590 $a School code: 0110.
650 4 $a Computer Science. $3 626642
650 4 $a Remote Sensing. $3 1018559
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710 2 0 $a University of Louisville. $3 1017614
773 0 $t Masters Abstracts International $g 42-01.
790 1 0 $a Elmaghraby, Adel S., $e advisor
790 $a 0110
791 $a M.S.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1415201