東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Clustering and reconstructing large ...

Kumar, Piyush.

Linked to FindBook

Google Book

Amazon

博客來

Clustering and reconstructing large data sets.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Clustering and reconstructing large data sets./
Author:	Kumar, Piyush.
Description:	247 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-10, Section: B, page: 5234.
Contained By:	Dissertation Abstracts International65-10B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3149113
ISBN:	0496086758

Clustering and reconstructing large data sets.
Kumar, Piyush.

Clustering and reconstructing large data sets. - 247 p.

Source: Dissertation Abstracts International, Volume: 65-10, Section: B, page: 5234.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2004.

This thesis deals with problems at the intersection of computational geometry, optimization, graphics, and machine learning. Geometric clustering is one such problem we explore. We develop fast approximation algorithms for clustering problems like the k-center problem and minimum enclosing ellipsoid problem based on the idea of core sets. We also explore an application of the 1-center problem to recognition of people based on their hand outlines.

ISBN: 0496086758Subjects--Topical Terms:

626642
Computer Science.

Clustering and reconstructing large data sets.
LDR:02143nmm 2200301 4500 001 1814578
005 20060719075826.5
008 130610s2004 eng d
020 $a 0496086758
035 $a (UnM)AAI3149113
035 $a AAI3149113
040 $a UnM $c UnM
100 1 $a Kumar, Piyush. $3 1253102
245 1 0 $a Clustering and reconstructing large data sets.
300 $a 247 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-10, Section: B, page: 5234.
500 $a Adviser: J. S. B. Mitchell.
502 $a Thesis (Ph.D.)--State University of New York at Stony Brook, 2004.
520 $a This thesis deals with problems at the intersection of computational geometry, optimization, graphics, and machine learning. Geometric clustering is one such problem we explore. We develop fast approximation algorithms for clustering problems like the k-center problem and minimum enclosing ellipsoid problem based on the idea of core sets. We also explore an application of the 1-center problem to recognition of people based on their hand outlines.
520 $a Another problem we consider in this thesis is how to reconstruct curves and surfaces from given sample points. We show implementations of algorithms that can handle noise for reconstructing curves in two dimensions. Based on Delaunay triangulations, we develop a surface reconstructor for a given set of sample points in three dimensions.
520 $a When dealing with massive data sets, it is important to consider the effect of memory hierarchies on algorithms. We explore this problem in our research on cache oblivious algorithms. We develop a practical cache oblivious algorithm to compute Delaunay triangulations of large point sets. We end the thesis with another optimization problem of approximately finding large empty convex bodies inside closed objects under various assumptions.
590 $a School code: 0771.
650 4 $a Computer Science. $3 626642
650 4 $a Artificial Intelligence. $3 769149
690 $a 0984
690 $a 0800
710 2 0 $a State University of New York at Stony Brook. $3 1019194
773 0 $t Dissertation Abstracts International $g 65-10B.
790 1 0 $a Mitchell, J. S. B., $e advisor
790 $a 0771
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3149113