Distributed dimension reduction algorithms for widely dispersed data
It is well known that information retrieval, clustering and visualization can often be improved by reducing the dimensionality of high dimensional data. Classical techniques offer optimality but are much too slow for extremely large databases. The problem becomes harder yet when data are distributed...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| التنسيق: | conferenceObject |
| منشور في: |
2002
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/7497 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php http://www.actapress.com/Abstract.aspx?paperId=24561 |
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| _version_ | 1864513482169253889 |
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| author | Abu-Khzam, F.N. |
| author2 | Samatova, N.F. Ostrouchov, G. Langston, M.A. Al Geist, G. |
| author2_role | author author author author |
| author_facet | Abu-Khzam, F.N. Samatova, N.F. Ostrouchov, G. Langston, M.A. Al Geist, G. |
| author_role | author |
| dc.creator.none.fl_str_mv | Abu-Khzam, F.N. Samatova, N.F. Ostrouchov, G. Langston, M.A. Al Geist, G. |
| dc.date.none.fl_str_mv | 2002 2018-04-24T09:27:43Z 2018-04-24T09:27:43Z 2018-04-24 |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10725/7497 Abu-Khzam, F. N., Samatova, N. F., Ostrouchov, G., Langston, M. A., & Geist, A. (2002). Distributed Dimension Reduction Algorithms for Widely Dispersed Data. In IASTED PDCS (pp. 167-174). http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php http://www.actapress.com/Abstract.aspx?paperId=24561 |
| dc.language.none.fl_str_mv | en |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Distributed dimension reduction algorithms for widely dispersed data |
| dc.type.none.fl_str_mv | Conference Paper / Proceeding info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/conferenceObject |
| description | It is well known that information retrieval, clustering and visualization can often be improved by reducing the dimensionality of high dimensional data. Classical techniques offer optimality but are much too slow for extremely large databases. The problem becomes harder yet when data are distributed across geographically dispersed machines. To address this need, an effective distributed dimension reduction algorithm is developed. Motivated by the success of the serial (non-distributed) FastMap heuristic of Faloutsos and Lin, the distributed method presented here is intended to be fast, accurate and reliable. It runs in linear time and requires very little data transmission. A series of experiments is conducted to gauge how the algorithm’s emphasis on minimal data transmission affects solution quality. Stress function measurements indicate that the distributed algorithm is highly competitive with the original FastMap heuristic. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | LAURepo_b0aaedddcc5ed7a4f97fb11c83dacfe6 |
| identifier_str_mv | Abu-Khzam, F. N., Samatova, N. F., Ostrouchov, G., Langston, M. A., & Geist, A. (2002). Distributed Dimension Reduction Algorithms for Widely Dispersed Data. In IASTED PDCS (pp. 167-174). |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/7497 |
| publishDate | 2002 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Distributed dimension reduction algorithms for widely dispersed dataAbu-Khzam, F.N.Samatova, N.F.Ostrouchov, G.Langston, M.A.Al Geist, G.It is well known that information retrieval, clustering and visualization can often be improved by reducing the dimensionality of high dimensional data. Classical techniques offer optimality but are much too slow for extremely large databases. The problem becomes harder yet when data are distributed across geographically dispersed machines. To address this need, an effective distributed dimension reduction algorithm is developed. Motivated by the success of the serial (non-distributed) FastMap heuristic of Faloutsos and Lin, the distributed method presented here is intended to be fast, accurate and reliable. It runs in linear time and requires very little data transmission. A series of experiments is conducted to gauge how the algorithm’s emphasis on minimal data transmission affects solution quality. Stress function measurements indicate that the distributed algorithm is highly competitive with the original FastMap heuristic.N/A2018-04-24T09:27:43Z2018-04-24T09:27:43Z20022018-04-24Conference Paper / Proceedinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://hdl.handle.net/10725/7497Abu-Khzam, F. N., Samatova, N. F., Ostrouchov, G., Langston, M. A., & Geist, A. (2002). Distributed Dimension Reduction Algorithms for Widely Dispersed Data. In IASTED PDCS (pp. 167-174).http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttp://www.actapress.com/Abstract.aspx?paperId=24561eninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/74972021-03-19T10:43:12Z |
| spellingShingle | Distributed dimension reduction algorithms for widely dispersed data Abu-Khzam, F.N. |
| status_str | publishedVersion |
| title | Distributed dimension reduction algorithms for widely dispersed data |
| title_full | Distributed dimension reduction algorithms for widely dispersed data |
| title_fullStr | Distributed dimension reduction algorithms for widely dispersed data |
| title_full_unstemmed | Distributed dimension reduction algorithms for widely dispersed data |
| title_short | Distributed dimension reduction algorithms for widely dispersed data |
| title_sort | Distributed dimension reduction algorithms for widely dispersed data |
| url | http://hdl.handle.net/10725/7497 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php http://www.actapress.com/Abstract.aspx?paperId=24561 |