A parallel varied density-based clustering algorithm with optimized data partition Frequent itemsets mining is one of the interesting applications of data mining. Recently data mining has got a great deal of attention due to the explosive... Y Gu,X Ye,F Zhang,... - 《Spatial Science》 被引...
It illustrates the performance of a binary classifier as its discrimination threshold is varied. From now on we refer to the recall, False Positive Rate and False Negative Rate as recall, FPR and FNR respectively. 4.5. Experimental comparisons It is worth noting here that, the goal of our ...
This might be because there have been missing statistical modeling approaches that can consider multiple factors from the dynamically changing traffic environment and the varied geometric conditions through different work zones. Also, real-time prediction in work zones hasn’t drawn sufficient attention. ...
This work presents a new density-based subspace clustering algorithm (S_FAD) to overcome the drawbacks of classical algorithms. S_FAD is based on a bottom-up approach and finds subspace clusters of varied density using different parameters of the DBSCAN algorithm. The algorithm optimizes parameters...
The accuracy of AF2 predictions provides opportunities for their use in experimental model building: (1) AF2 models could be used for molecular replacement or docking into cryo-EM density, experimental phasing and/or ab initio model building; and (2) they could be used as reference points to ...
a, The results of computationally varying the label density on some of the simulation systems. b, The results of computationally varying the label density on AKAP79 and AKA150. (Values greater than 1 indicate significant clustering.). Source data Extended Data Fig. 9 Experimental Concerns. Image...
First, a spatial-temporal density based clustering algorithm (ST-DBCA) is proposed for trip end identification, the method considers both spatial and temporal GPS trajectory density at the same time, and performs much better than traditional methods. Second, three optimization models are further ...
Partitional:PAM(Partition Around Medoid, Rousseeuw and Kaufman1987) andTClust(a robust adaptation of theK-meansalgorithm, García-Escudero et al.,2008). b) Hierarchical clustering:HClust(Murtagh,1983). c) Density-based:DBSCAN(Density-Based Spatial Clustering of Applications with Noise, Ester et al...
Martin R (2001) Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE Trans Speech Audio Process 9:504–512 Article Google Scholar Nadipineni, H (2020) Method to classify skin lesions using dermoscopic images. arXiv preprint arXiv:2008.09418 Najafi A, ...
Broadband PLC modems estimate the DSL-to-PLC channel interference and adapt the PLC’s transmit power spectral density accordingly. Moreover, a considerable effort has been made in PLC focused on the physical layer to deal with issues such as the time-varying behaviour of loads in electric ...