Algorithms for data mining
Name
154315614-MIT.pdf
Description
Full printable version
Size
5.65 MB
Format
Adobe PDF
Checksum (MD5)
7046915db1f6acbad4b00cf59a5a8924
Author(s)
Wang, Grant J. (Grant Jenhorn), 1979-
Advisor(s)
Santosh Vempala.
Date Issued
2006
Publisher
Massachusetts Institute of Technology
Abstract
Data of massive size are now available in a wide variety of fields and come with great promise. In theory, these massive data sets allow data mining and exploration on a scale previously unimaginable. However, in practice, it can be difficult to apply classic data mining techniques to such massive data sets due to their sheer size. In this thesis, we study three algorithmic problems in data mining with consideration to the analysis of massive data sets. Our work is both theoretical and experimental - we design algorithms and prove guarantees for their performance and also give experimental results on real data sets. The three problems we study are: 1) finding a matrix of low rank that approximates a given matrix, 2) clustering high-dimensional points into subsets whose points lie in the same subspace, and 3) clustering objects by pairwise similarities/distances.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 81-89).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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