A similarity measure based on multiple clustering is proposed. The similarity between two sample data is defined as the probability that the two samples are classified into the same cluster. In practice, assumin
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DUBStepR is a scalable correlation-based feature selection method for accurately clustering single-cell data Article Open access 06 October 2021 Introduction Technological advances of large-scale single-cell profiling of genes and proteins, such as single-cell RNA-seq (scRNA-seq)1, Cytometry by Tim...
美 英 un.抽样资料;采样数据;样本数据 网络数据采样法 英汉 网络释义 un. 1. 抽样资料 2. 采样数据 3. 样本数据
- Unix/Linux System - Python 2.6 or above - R 3.1 or above (required to generate a PDF of sample clustering dendrogram and a xgmml graphical output for sample clustering; see Output and Supporting scripts) For the BAM module, - samtools (tested on version 0.1.19 and 1.3.1) - bcftools ...
In addition, stratification and clustering can be combined to create complex survey designs. For example, the country could be divided into mutually exclusive quadrants of approximately the same geographic size. An equal number of schools could be selected within each quadrant, ensuring that the ...
It has been accepted that a single clustering algorithm can not handle all types of data distribution effectively. Each clustering algorithm has its own strategy to discover a structure from a data set. Different algorithms or different parameters for an algorithm may lead to different clustering ...
Hagras, H.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Trans. Fuzzy Syst. 12, 524–539 (2004) CrossRef John, R., Innocent, P., Barnes, M.: Neuro-fuzzy clustering of radiographic tiba image data using type-2 fuzzy sets. Inf. Sci. 125,...
A common feature of these studies is a non-standard data structure with repeated measurements which may have some degree of clustering. In this paper, methodology is presented for the joint estimation of quantities of interest in the context of a stratified two-stage sample with bivariate ...
these analytical methods, there is a high demand for classifying the sample data and detecting outliers using data characteristics that support the receptor models above. Clustering methods are an area of prolific research in data mining, and have been known to produce more natural classifica...