Clustering Main Concepts from e-Mails
E–mail is one of the most common ways to communicate, assuming, in some cases, up to 75% of a company’s communication, in which every employee spends about 90 minutes a day in e–mail tasks such as filing and deleting. This paper deals with the generation of clusters of relevant words from E–mail tex...
| Autores: | , , , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2003 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39340 |
| Acceso en línea: | http://hdl.handle.net/11441/39340 https://doi.org/10.1007/978-3-540-25945-9_23 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial Intelligence (incl. Robotics) Mathematical Logic and Formal Languages Computation by Abstract Devices |
| Sumario: | E–mail is one of the most common ways to communicate, assuming, in some cases, up to 75% of a company’s communication, in which every employee spends about 90 minutes a day in e–mail tasks such as filing and deleting. This paper deals with the generation of clusters of relevant words from E–mail texts. Our approach consists of the application of text mining techniques and, later, data mining techniques, to obtain related concepts extracted from sent and received messages. We have developed a new clustering algorithm based on neighborhood, which takes into account similarity values among words obtained in the text mining phase. The potential of these applications is enormous and only a few companies, mainly large organizations, have invested in this project so far, taking advantage of employees’s knowledge in future decisions. |
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