K-Means ClusteringHierarchical ClusteringExpectation and Maximization AlgorithmGrid Based CalculationClustering or group examination can be considered as a key unit in information investigation, whose primary point is to isolate the information, informational iGoyal, Yogita...
54 Solving clustering problems via new swarm intelligent algorithms 42:45 Statistical Network Models for Integrating Functional Connectivity with sMRI and 59:08 The Tumor Growth Paradox 46:37 A case study on stochastic games on large graphs in mean field and sparse regime 48:17 Differential ...
The problem at hand is to create a system that can handle the subjective nature of qualitative personality data, providing insights into how people interact, collaborate, and behave in various social contexts and thus increase learning opportunities. To achieve this, variou...
In this work we focus attention on the single-label setting and we call the considered learning task multi-type clustering and classification from heterogeneous networks. This task is not completely new in the literature and has connections with the task of multiple predicate learning [36] in ILP...
Multiple iterations of clustering and annotation were performed until signal-noise ratio was too low to confidently distinguish the phenotype of the remaining cells, which were assigned as “Others”. 140,053 out of 145,161 cells (96.5%) were assigned a final annotation. All final annotations ...
Research on Multi-view Clustering Data Mining Algorithms Based on Microblog Micro-blog is a new type of social network platform emerging in recent years. With the development of the Internet and the continuous popularization of mobile personal terminals, the number of users and content of microblog...
A novel joint clustering and classification (JCC) method which could discover hidden clusters features in the patient samples was developed to predict diabetes, and the method performed best among the methods that were applicable to the interpretation of prediction22. A study used neural network, ...
Shuliang X, Lin F, Shenglan L et al (2020) Self-adaption Neighborhood Density Clustering Method for Mixed Data Stream with Concept Drift[J]. Eng Appl Artif Intell 89:1–14 Google Scholar Meng H, Zhihai W, Jian D (2016) A frequent pattern decision tree deals with variable data streams...
We neglected the minor cell types, which means those novel subtypes uncovered by scRNA-seq with unsupervised clustering and annotation (e.g., considering both “DC_cluster1” and “DC_cluster2” as DC cells). False positive control for cell type assignment The basic idea is to quantify the ...
Cell type annotation can resolve cellular heterogeneity across tissues, developmental stages and organisms, and improve our understanding of cellular and gene functions in health and disease. Many unsupervised scRNA-seq clustering methods have been proposed2,3,4, which are followed by time-consuming ...