Jain, A.K. (2008). Data Clustering: 50 Years Beyond K-means. In: Daelemans, W., Goethals, B., Morik, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science(), vol 5211. Springer, Berlin, Heidelberg. https://doi.org/10.1007...
Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been ...
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 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)...
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...
The K-means algorithm is the most fundamental partitional clustering concept which was published by Lloyd of BellTelephonelaboratories in 1957[373–375]. After 50 years of its existence, till date this algorithm is still popular and widely used for high dimensional datasets due to its simplicity ...
# 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) ...
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 ...
Specific values of k were selected, and the resulting shape and topology of the datascape were included in the visualization. Full size image Spatial dataset: clustering and geodesic analysis of the global ocean Ocean studies tackle some of the most significant spatial data. This field relies on...
We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic...