Parallel Co-location Pattern Mining in Cloud for GIS Application - Eman Refaye
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Toimitus 12-18 arkipäivässä
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Spatial data mining become one of the important areas because of the rapid evolution in technology which leads in big spatial data. Co-locations pattern mining is an interesting and important issue in spatial data mining area which discovers the subsets of features whose events are frequently located together in geographic space. Spatial proximity is the important concept to determine the Col-ocation patter ... Täydellinen kuvaus
Kuvaus
Spatial data mining become one of the important areas because of the rapid evolution in technology which leads in big spatial data. Co-locations pattern mining is an interesting and important issue in spatial data mining area which discovers the subsets of features whose events are frequently located together in geographic space. Spatial proximity is the important concept to determine the Col-ocation patterns from massive data. The computation of co-location pattern discovery is very expensive with big data volume and nearby existence of neighborhoods. So there is number of spatial co-location mining algorithms have been developed to overcome these drawbacks.
Lisätietoja
| Kirjoittaja | Eman Refaye |
|---|---|
| Julkaisija | LAP LAMBERT Academic Publishing |
| Julkaisuvuosi | 2016 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9783330020474 |