Single pass kernel k-means clustering method. Sadhana-Acad. P. Eng. S., 38(6), 407-419.T Hitendra Sarma1, P Viswanath2 and B Eswara Reddy3, " Single pass kernel k-means clustering method" Sadhana Vol. 38, Part 3, June 2013, pp. 407-419._c Indian Academy of Sciences....
After the clustering process has been performed, a single representative variable can be extracted from each group. Suppose that the available x data are disposed in a matrix X of dimensions (N × K) such that the kth variable xk is associated to the kth column vector xk∈RN. This vector...
A common analysis of single-cell sequencing data includes clustering of cells and identifying differentially expressed genes (DEGs). How cell clusters are defined has important consequences for downstream analyses and the interpretation of results, but is often not straightforward. To address this difficu...
CASCC: A co-expression assisted single-cell RNA-seq data clustering method CASCC, a clustering method designed to improve biological accuracy using gene co-expression features identified using an unsupervised adaptive attractor algorithm. The users can refer to our published manuscript, available at th...
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is...
However, existing clustering methods are often presented in a single-layer formulation (i.e., shallow formulation). As a result, the mapping between the obtained low-level representation and the original input data may contain rather complex hierarchical information. To overcome the drawbacks of low...
However, instead of following some of the single clustering algorithm procedures in building a consensus function, we are using the generated members as initial clusters of the dataset and the final clustering is generated in three stages, as shown in Fig. 2. The first stage is to transfer the...
jointDIMMSCis developed as an extension of DIMMSC, which assumes full indenpendency between single cell RNA and surface protein data. We construct the joint likelihood of the two data sources as their product, and use EM algorithm for parameter inference. In practice, the computational speed for...
AGNES initially takes each object as a cluster, afterwards the clusters are merged step by step according to certain criteria, using a single-link method. The level of similarity of the two clusters is measured by the similarity of the nearest pair of data points in the two different clusters...
Image segmentation methods based on clustering have become a advancement in the field of computer vision, and the performance of these methods mainly depends on the performance of the clustering algorithm. However, these methods have some shortcomings. First, a single feature cannot accurately obtain ...