Clustering of spatial data for knowledge extraction
| Autor: | Ademir Freddo, Francisco Reinaldo, Luís Paulo Reis, Marcos Ribeiro, Eduardo S. Martins, Jugurta Lisboa-Filho |
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| Rok vydání: | 2016 |
| Předmět: |
Data element
Information retrieval 010504 meteorology & atmospheric sciences Data stream mining Computer science Concept mining 010502 geochemistry & geophysics computer.software_genre 01 natural sciences Data mapping Knowledge extraction Web mining Software mining Data mining Cluster analysis computer 0105 earth and related environmental sciences |
| Zdroj: | 2016 11th Iberian Conference on Information Systems and Technologies (CISTI). |
| DOI: | 10.1109/cisti.2016.7521626 |
| Popis: | Spatial Data Infrastructures (SDI) are repositories of large volumes of data, documented through standardized metadata. Data mining is one of the main techniques used to extract knowledge from large amounts of data, because of its versatility. The purpose of this article is to use clustering techniques and data mining to extract relationships and knowledge from metadata in SDI. For this reason, knowledge discovery techniques, clustering, text mining and data mining algorithms were used. In order to demonstrate the effectiveness of the proposed method, a case study was implemented to evaluate the performance of data mining techniques in this type of database. The results showed that the data mining process and clustering techniques guided to the classification proposed method for extracting relations and knowledge from a group of metadata extracted from within the database. |
| Databáze: | OpenAIRE |
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