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Clustering and reconstructing large ...

Kumar, Piyush.

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Clustering and reconstructing large data sets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Clustering and reconstructing large data sets./
作者:	Kumar, Piyush.
面頁冊數:	247 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-10, Section: B, page: 5234.
Contained By:	Dissertation Abstracts International65-10B.
標題:	Computer Science. -
電子資源:	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.
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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.
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