This paper focuses on document clustering by clustering algorithm based on a DEnsityTree (CABDET) to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential c...This paper focuses on document clustering by clustering algorithm based on a DEnsityTree (CABDET) to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential cluster by dynamically adjusting the radius of neighborhood according to local density. It avoids density-based spatial clustering of applications with noise (DBSCAN) ′s global density parameters and reduces input parameters to one. The results of experiment on real document show that CABDET achieves better accuracy of clustering than DBSCAN method. The CABDET algorithm obtains the max F-measure value 0.347 with the root node's radius of neighborhood 0.80, which is higher than 0.332 of DBSCAN with the radius of neighborhood 0.65 and the minimum number of objects 6.展开更多
The Dehesa ecosystem provides important social and economic values across the Iberian Peninsula.Assessing the temporal dynamics of this system under climate change is important for the maintenance and conservation of ...The Dehesa ecosystem provides important social and economic values across the Iberian Peninsula.Assessing the temporal dynamics of this system under climate change is important for the maintenance and conservation of these highly valuable ecosystems.Here,we present the baseline data of an observational plot network in the Dehesa that will form the foundation for monitoring long-term dynamics and for experimental manipulations testing the mechanisms driving resilience within the Dehesa.The initial surveys indicate that the forest structure is typical for the Dehesa,which suggests it is an exemplary site for examining temporal dynamics of this ecosystem.We present these initial data to encourage collaborations from international scientists via either direct experimental projects or meta-analyses.展开更多
基金Science and Technology Development Project of Tianjin(No. 06FZRJGX02400)National Natural Science Foundation of China (No.60603027)
文摘This paper focuses on document clustering by clustering algorithm based on a DEnsityTree (CABDET) to improve the accuracy of clustering. The CABDET method constructs a density-based treestructure for every potential cluster by dynamically adjusting the radius of neighborhood according to local density. It avoids density-based spatial clustering of applications with noise (DBSCAN) ′s global density parameters and reduces input parameters to one. The results of experiment on real document show that CABDET achieves better accuracy of clustering than DBSCAN method. The CABDET algorithm obtains the max F-measure value 0.347 with the root node's radius of neighborhood 0.80, which is higher than 0.332 of DBSCAN with the radius of neighborhood 0.65 and the minimum number of objects 6.
基金M.J.O.was funded by the Comunidad de Madrid with an Atraccion de Talento Investigador Modalidad I Fellowship(2018-T1/AMB-11095).
文摘The Dehesa ecosystem provides important social and economic values across the Iberian Peninsula.Assessing the temporal dynamics of this system under climate change is important for the maintenance and conservation of these highly valuable ecosystems.Here,we present the baseline data of an observational plot network in the Dehesa that will form the foundation for monitoring long-term dynamics and for experimental manipulations testing the mechanisms driving resilience within the Dehesa.The initial surveys indicate that the forest structure is typical for the Dehesa,which suggests it is an exemplary site for examining temporal dynamics of this ecosystem.We present these initial data to encourage collaborations from international scientists via either direct experimental projects or meta-analyses.
