2026-09-19T10:14:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1131682026-06-06T00:48:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Samuel R. Madden.
Taratoris, Evangelos.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-01-12T21:15:11Z
2018-01-12T21:15:11Z
2017
2017
http://hdl.handle.net/1721.1/113168
1017485602
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 79-80).
The problem of clustering multi-dimensional data has been well researched in the scientific community. It is a problem with wide scope and applications. With the rapid growth of very large databases, traditional clustering algorithms become inefficient due to insufficient memory capacity. Grid-based algorithms try to solve this problem by dividing the space into cells and then performing clustering on the cells. However these algorithms also become inefficient when even the grid becomes too large to be saved in memory. This thesis presents a new algorithm, SingleClus, that is performing clustering on a 2-dimensional dataset with a single pass of the dataset. Moreover, it optimizes the amount of disk I/0 operations while making modest use of main memory. Therefore it is theoretically optimal in terms of performance. It modifies and improves on the Hoshen-Kopelman clustering algorithm while dealing with the algorithm's fundamental challenges when operating in a Big Data setting.
by Evangelos Taratoris.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
80 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A single-pass grid-based algorithm for clustering big data on spatial databases
Thesis
application/pdf
2019-06-17T20:35:52Z
Publication
unknown
unknown
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<Abstract>The problem of clustering multi-dimensional data has been well researched in the scientific community. It is a problem with wide scope and applications. With the rapid growth of very large databases, traditional clustering algorithms become inefficient due to insufficient memory capacity. Grid-based algorithms try to solve this problem by dividing the space into cells and then performing clustering on the cells. However these algorithms also become inefficient when even the grid becomes too large to be saved in memory. This thesis presents a new algorithm, SingleClus, that is performing clustering on a 2-dimensional dataset with a single pass of the dataset. Moreover, it optimizes the amount of disk I/0 operations while making modest use of main memory. Therefore it is theoretically optimal in terms of performance. It modifies and improves on the Hoshen-Kopelman clustering algorithm while dealing with the algorithm's fundamental challenges when operating in a Big Data setting.</Abstract>
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