In this paper, we introduce the algorithm ClicoT (CLustering mixed-type data Including COncept Trees) as reported by Behzadi et al. (Advances in Knowledge Discovery and Data Mining, Springer, Cham, 2019) which is based on the minimum description length principle. Profiting of the conceptual ...
Experimental results on Movie database show the effectiveness of this method.GAO YingLIU DaYouQI HongLIU He高滢刘大有齐红刘赫软件学报GAO Ying,LIU Da-You,QI Hong,LIU He."Semi-Supervised K-Means ClusteringAlgorithm for Multi-Type Relational Data". Journal of Software . 2008...
To address this difficulty, in this paper a two-stage clustering algorithm for multi-type relational data (TSMRC) has been proposed Based on the analysis of data and relationships, TSMRC has two stages, which are benefit to improve the efficiency of clustering. To improve the quality of ...
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 ...
D2 clustering algorithm. Examining global effects of identified variants on complex phenotypes To verify that Huatuo enables identifications of the genetic regulation that serves as potential functional mechanisms underlying the GWAS associations of complex diseases and traits, we constructed logistic ...
Since clusters are used for predictive purposes, a clustering algorithm which takes as input the number K of clusters would affect the predictive capabilities of the learned models. In particular, setting K equal to the number of classes would lead to identifying models which are too general and...
The method using fuzzy C-mean clustering algorithm and the back-propagation technique to determine parameters of type-2 TSK fuzzy classifier was presented. The generalized bell primary membership function was used to examine the performance of the classifier with different shapes of membership functions...
one attempts to determine the best transformation for aligning similar images. Such problems typically require minimizing a dissimilarity measure with multiple local minima. We describe a global optimization algorithm and apply it to the problem of identifying the best transformation for aligning two image...
TheItClustpackage is an implementation of Iterative Transfer learning algorithm for scRNA-seq Clustering. With ItClust, you can: Preprocess single cell gene expression data from various formats. Build a network for target data clustering with prioe knowledge learnt from the source data. ...
For input to our method, we use post-quality control scRNA-seq data with unique molecular identifier counts, a target marker set size, and a hierarchical taxonomy of cell labels. When cell labels do not exist, labels may be inferred using a clustering algorithm, or via another data modality...