| dc.creator | Rivera Hazim, Edwardo S. | |
| dc.creator | Orozco, Edusmildo (Mentor) | |
| dc.creator | Ordóñez, P. (Mentor) | |
| dc.date | 2013-03-13T19:33:02Z | |
| dc.date | 2013-03-13T19:33:02Z | |
| dc.date | 2013-03-01 | |
| dc.date.accessioned | 2017-03-17T16:53:58Z | |
| dc.date.available | 2017-03-17T16:53:58Z | |
| dc.identifier | http://hdl.handle.net/10586 /318 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/647457 | |
| dc.description | Data mining algorithms are crucial in the analysis of many real data-intensive problems. The Symbolic Aggregate Approximation (SAX) algorithm is being used widely by researchers to analyze time series and streaming data. Previous work done by P. Ordóñez et. al. has shown that it is possible to classify a patient's data by using a combination of SAX and the Bag-of-Patterns algorithm (BoP). To our knowledge, parallel implementations for these algorithms have not been proposed yet. | |
| dc.description | National Science Foundation, XXVIII Seminario Interuniversitario de Investigación en Ciencias Matemáticas, Universidad Metropolitana, UPR | |
| dc.language | en_US | |
| dc.publisher | Seminario Interuniversitario de Investigación en Ciencias Matemáticas (SIDIM) | |
| dc.subject | Symbolic Aggregate Approximation | |
| dc.subject | NR Algorithm | |
| dc.subject | time series | |
| dc.subject | streaming data. | |
| dc.title | On Parallel Methods for Classifying Time Series Data | |
| dc.type | Otro | |
