Even so interesting, no multivariate method has been put forward for microarray data analysis. According to the recent published research, graph clustering-based discretization of splitting and merging methods (GraphS and GraphM) usually achieves superior results compared to many well-known discretization...
基于图的聚类集成与数据可视化分析-graph - based clustering integration and data visualization analysis.docx,摘要聚类分析是一门重要学科,其依据测量对象的内在特性或相似度将对象进行分组,在多种社会科学领域中都有应用,如数据压缩、数据挖掘、图像分割和信息检索
This allows employing our approach in downstream machine learning and data mining methods such as nearest neighbors classification, local outlier detection (Schubert et al. 2014), or density-based clustering (Ester et al. 1996). (4) Experimental Evaluation. We show that, while the proposed bounds...
The complexity of the data and the lack of certainty about the relevant cases claims new strategies that allow us to get insights into their underlying role in tumorigenesis. Here we have proposed a statistical framework to fulfill this gap. First, we used a KDE-based clustering method ...
Her research interests are data mining, information retrieval and machine learning.References (28) S. Schaeffer Graph clustering Comput. Sci. Rev. (2007) S. Fortunato Community detection in graphs Phys. Rep. (2010) M. Fellows et al. Graph-based data clustering with overlaps Discrete Optim. (...
Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construct
The global clustering coefficient evaluates the global clustering degree in a graph; the local aggregation coefficient measures the degree to which a vertex is connected to its neighbors in a graph; the average aggregation coefficient provides an average indication of the clustering in a graph based ...
3.2 The bipartite graph based clustering After fusing these representation matrices, we can obtain a final representation or coefficient matrix Z∈Rm×n to construct the bipartite graph. This coefficient matrix reflects the relationships between the original data samples and anchor data samples well, so...
Zhang Y, Zhang F, Yao P, et al.Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. KDD2018: 1002-1011. Ngomo A C N, Auer S.LIMES—a time-efficient approach for large-scale link discovery on the web of data. IJCAI2011. ...
论文阅读06——《CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering》 Ideas: Model: 交叉注意力融合模块 图自编码器 Ideas: 提出一种基于端到端的交叉注意力融合的深度聚类框架,其中交叉注意力融合模块创造性地将图卷积自编码器模块和自编码器模块多层级连起来 ...