Disadvantages: the premise not completely correct, involved in many parameters which have a strong influence on the clustering result and relatively high time complexity. Table 9 Time complexity Full size table
The Sparse Signal Superdensity (\(\textbf{S}^3\))Huang et al. (2021) framework, instead, addresses the challenges of low density and imbalanced distribution of sparse depth cues by expanding the depth estimates around sparse signals based on the RGB image and controlling their influence through...
should perform real-time forecasting. Moreover, given the complexity of implementing a stream-based forecasting system and a forecasting algorithm, researchers can be more focused on developing one of these tasks when they publish their work. The same can be applied to anomaly detection concepts and...
proposed SAPAS for identifying pAs from poly(A)-containing reads and quantifying pAs in peak regions determined by a parametric clustering algorithm [102]. They further applied SAPAS to the scRNA-seq data of GABAergic neurons and detected cell type-specific APA events and cell-to-cell modality...
Cluster heads are nominated by the centralised scheduling algorithm based on their betweenness centrality metric, computing performance, and communication delay to service requesters. Heads subsequently run aggregation of other fog nodes through application of spectral clustering methods. Fog nodes of low ...
designed an algorithm to detect which means of transport people would choose, including public transportation or private means, to infer how many people used which public transportation routes [121] throughout the day. The authors then proposed a model of the network of local transportation of Abid...
To cluster single cells by their expression, we used an unsupervised clustering approach, based on the Infomap graph-clustering algorithm9, following approaches for single-cell CyTOF data57 and scRNA-seq10. In brief, we constructed a k-nearest-neighbour graph on the data using, for each pair ...
3.3.1 Clustered process Vehicles on typical roads tend towards clustering, due to traffic congestion and intersections [41,42]. Point processes with the attraction between points are proposed to more accurately describe the vehicular distribution than PPP. The typical attractive models are the Cox ...
In preparation for this, the k-means clustering algorithm is applied to the real \(D_r\) in order to create a partition \(\Pi\), creating k subsets referred to as “bins”. The synthetic \(D_g\) is split into k bins in a similar fashion using the cluster centers of \(\Pi\)....
clustering (SC)’s limitations, various extensions based on power iteration have been developed, such as Arnoldi iteration [132], the Lanczos method [133], Implicitly Restarted Lanczos Method [134] and Krylov-Schur algorithm [135].(2)Sampling approach: Sampling approaches [136] work on a ...