Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, a
Kumar GR, Mangathayaru N, Narasimha G (2015) An improved k-means clustering algorithm for intrusion detection using Gaussian function. In: Proceedings of the the international conference on engineering & MIS 2015. ACM, p 69 Landress AD (2016) A hybrid approach to reducing the false positive ...
In preparation for this, the k-means clustering algorithm is applied to the real [Math Processing Error]Dr in order to create a partition [Math Processing Error]Π, creating k subsets referred to as “bins”. The synthetic [Math Processing Error]Dg is split into k bins in a similar ...
Tthe given task, and\(M_x\)a trained ML model for algorithmA. This figure serves as an orientation for other train on synthetic, test on real (TSTR)-based measures as well
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...
It is then possible to assign each line one of the events as indicated on the right side of the figure. In such a setting, the result of a dynamic clustering algorithm could be the extracted sequence A, B, C since this pattern describes normal user behavior. However, the events in ...
, Isolation of tuft-2 cells based on CD45 expression using FACS. Shown is t-SNE of 332 EpCAM+/CD45+ FACS-sorted single cells (points; n = 3 pooled mice), coloured by unsupervised clustering (top left), the expression of the Tuft cell marker Dclk1 (top right), or the ...
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...
For example, we can have algorithm instability, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors regarding statistical prob- lems [2]. On the other hand, regarding computation problems, we have storage, scal- ability, and bottleneck problems [2, 79]. ...
project them into the vector space. At present, some mainstream clustering algorithms mainly include the K-means algorithm (K-means), the nearest neighbor algorithm (KNN), the density-based clustering algorithm (DBSCAN), and the maximum expectation (EM) clustering using Gaussian Mixture Model (GMM...