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A minimum spanning tree based cluste...

Pirim, Harun.

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A minimum spanning tree based clustering algorithm for high throughput biological data.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A minimum spanning tree based clustering algorithm for high throughput biological data./
作者:	Pirim, Harun.
面頁冊數:	107 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
Contained By:	Dissertation Abstracts International72-06B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3450335
ISBN:	9781124589022

A minimum spanning tree based clustering algorithm for high throughput biological data.
Pirim, Harun.

A minimum spanning tree based clustering algorithm for high throughput biological data. - 107 p.

Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .

Thesis (Ph.D.)--Mississippi State University, 2011.

A new minimum spanning tree (MST) based heuristic for clustering biological data is proposed. The heuristic uses MSTs to generate initial solutions and applies a local search to improve the solutions. Local search transfers the nodes to the clusters with which they have the most connections, if this transfer improves the objective function value. A new objective function is defined and used in the heuristic. The objective function considers both tightness and separation of the clusters. Tightness is obtained by minimizing the maximum diameter among all clusters. Separation is obtained by minimizing the maximum number of connections of a gene with other clusters. The objective function value calculation is realized on a binary graph generated using the threshold value and keeping the minimum percentage of edges while the binary graph is connected. Shortest paths between nodes are used as distance values between gene pairs. The efficiency and the effectiveness of the proposed method are tested using fourteen different data sets externally and biologically. The method finds clusters which are similar to actual ones using 12 data sets for which actual clusters are known. The method also finds biologically meaningful clusters using 2 data sets for which real clusters are not known. A mixed integer programming model for clustering biological data is also proposed for future studies.

ISBN: 9781124589022Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

A minimum spanning tree based clustering algorithm for high throughput biological data.
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245 1 2 $a A minimum spanning tree based clustering algorithm for high throughput biological data.
300 $a 107 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: .
500 $a Adviser: Burak Eksioglu.
502 $a Thesis (Ph.D.)--Mississippi State University, 2011.
520 $a A new minimum spanning tree (MST) based heuristic for clustering biological data is proposed. The heuristic uses MSTs to generate initial solutions and applies a local search to improve the solutions. Local search transfers the nodes to the clusters with which they have the most connections, if this transfer improves the objective function value. A new objective function is defined and used in the heuristic. The objective function considers both tightness and separation of the clusters. Tightness is obtained by minimizing the maximum diameter among all clusters. Separation is obtained by minimizing the maximum number of connections of a gene with other clusters. The objective function value calculation is realized on a binary graph generated using the threshold value and keeping the minimum percentage of edges while the binary graph is connected. Shortest paths between nodes are used as distance values between gene pairs. The efficiency and the effectiveness of the proposed method are tested using fourteen different data sets externally and biologically. The method finds clusters which are similar to actual ones using 12 data sets for which actual clusters are known. The method also finds biologically meaningful clusters using 2 data sets for which real clusters are not known. A mixed integer programming model for clustering biological data is also proposed for future studies.
520 $a Key words: clustering, optimization, heuristics, networks, integer programming
590 $a School code: 0132.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Operations Research. $3 626629
690 $a 0715
690 $a 0796
710 2 $a Mississippi State University. $b Industrial and Systems Engineering. $3 1023487
773 0 $t Dissertation Abstracts International $g 72-06B.
790 1 0 $a Eksioglu, Burak, $e advisor
790 1 0 $a Greenwood, Allen $e committee member
790 1 0 $a Perkins, Andy D. $e committee member
790 1 0 $a Yuceer, Cetin $e committee member
790 1 0 $a Jin, Mingzhou $e committee member
790 $a 0132
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3450335