Perspectives on clusteringKing-Sun Fu prizeThe practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of ...
Data clustering:50years beyond k-means翻译K-means后数据聚类的50年发展 Anil K.Jain密歇根州立大学计算机科学与工程系高丽大学大脑与认知工程系 翻译人徐天宇专业班级自动化1104 . 摘要:数据进行合理的聚群是理解和学习最基本的模式之一。例如,一个常见的科学分类将生物归类为如下的类别体系:域、界、门、纲、目...
Data clustering: 50 years beyond K-means,Basic RGB 0167-8655/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.patrec.2009.09.011 q This paper is based on the King-Sun Fu Prize l International Conferenc... AK Jain 被引量: 0发表: 2010年 MIPAS detects Antarctic stratospheric belt ...
Data clustering: 50 years beyond k-means Pattern Recognit. Lett. (2010) R. Liu et al. Shared-nearest-neighbor-based clustering by fast search and find of density peaks Inf. Sci. (2018) N.C. Sandes et al. Clustering ensembles: a hedonic game theoretical approach Pattern Recognit. (2018)...
Data clustering: 50 years beyond K-means. Pattern Recognit. Lett. 31, 651–666 (2010). Google Scholar Quake, S. R., Wyss-Coray, T., Darmanis, S. & The Tabula Muris Consortium. Transcriptomic characterization of 20 organs and tissues from mouse at single cell resolution creates a ...
The only information clustering uses is the similarity between examples Clustering groups examples based of their mutual similarities A good clustering is one that achieves:High within-cluster similarityLow inter-cluster similarityPicture courtesy: "Data Clustering: 50 Years Beyond K-Means", A.K. Jain...
Data clustering: 50 years beyond K-means Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific cl... AK Jain - 《Pattern Recognition Letters》 被引量: 4374发表: 2010年 Implementing the Fisher'...
Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive ...
# Perform clustering with a different number of clustersR=range(50,1000,50) KM = (cluster.KMeans(n_clusters=k).fit(data)forkinR) Then determine the error for each case and visualize the data obtained. distance=(k.transform(data)forkinKM) ...
Data clustering: 50 years beyond k-means. Pattern Recognition Letter, 31(8):651-666, June 2010.A. K. Jain, "Data clustering: 50 years beyond K-means," Pat- tern Recognition Lett., vol. 31, no. 8, pp. 651-666, June 2010.