2026-09-18T23:18:33Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/383152022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Santosh Vempala.
Wang, Grant J. (Grant Jenhorn), 1979-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2007-08-03T18:29:12Z
2007-08-03T18:29:12Z
2006
2006
http://hdl.handle.net/1721.1/38315
154315614
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 81-89).
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.
by Grant J. Wang.
Ph.D.
89 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Algorithms for data mining
Thesis
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