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Efficiently searching and mining bio...

Zhou, Jianjun.

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Efficiently searching and mining biological sequence and structure data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Efficiently searching and mining biological sequence and structure data./
Author:	Zhou, Jianjun.
Description:	115 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0454.
Contained By:	Dissertation Abstracts International71-01B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR55643
ISBN:	9780494556436

Efficiently searching and mining biological sequence and structure data.
Zhou, Jianjun.

Efficiently searching and mining biological sequence and structure data. - 115 p.

Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0454.

Thesis (Ph.D.)--University of Alberta (Canada), 2009.

The rapid growth of biological sequence and structure data imposes significant challenges on searching and mining them. While handling growing data-sets has been a continuously interesting topic in the database and data mining communities, the unique characteristics of biological data make it difficult or even impossible to directly apply traditional database searching and mining methods. In many biological databases, the data objects and the dissimilarity measurement (i.e. distance function) between data objects form a so-called metric space, in which the notions of dimensionality in traditional vector space are no longer valid and the dissimilarity measurement can be computationally much more expensive than traditional measurements such as the Euclidean distance in a low dimensional space.

ISBN: 9780494556436Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Efficiently searching and mining biological sequence and structure data.
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008 130515s2009 ||||||||||||||||| ||eng d
020 $a 9780494556436
035 $a (UMI)AAINR55643
035 $a AAINR55643
040 $a UMI $c UMI
100 1 $a Zhou, Jianjun. $3 1671193
245 1 0 $a Efficiently searching and mining biological sequence and structure data.
300 $a 115 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-01, Section: B, page: 0454.
502 $a Thesis (Ph.D.)--University of Alberta (Canada), 2009.
520 $a The rapid growth of biological sequence and structure data imposes significant challenges on searching and mining them. While handling growing data-sets has been a continuously interesting topic in the database and data mining communities, the unique characteristics of biological data make it difficult or even impossible to directly apply traditional database searching and mining methods. In many biological databases, the data objects and the dissimilarity measurement (i.e. distance function) between data objects form a so-called metric space, in which the notions of dimensionality in traditional vector space are no longer valid and the dissimilarity measurement can be computationally much more expensive than traditional measurements such as the Euclidean distance in a low dimensional space.
520 $a In this thesis, we study the problems of performing efficient clustering and similarity searches on biological sequence and structure data using an expensive distance function. The efficient solutions to these problems relies on the ability of the searching and mining algorithms to avoid expensive distance computations. For this central challenge, we propose several novel techniques including directional extent in non-vector data bubbles, pairwise ranking, virtual pivots and partial pivots. In-depth theoretical studies and extensive experimental results on several real-life data-sets confirm the superiority of our methods over the previous approaches.
590 $a School code: 0351.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Computer Science. $3 626642
690 $a 0715
690 $a 0984
710 2 $a University of Alberta (Canada). $3 626651
773 0 $t Dissertation Abstracts International $g 71-01B.
790 $a 0351
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR55643