Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Ulf Brefeld,Elisa Fromont,Céline Robardet Conference proceeding

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書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)




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書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次




書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學(xué)科排名




書目名稱Machine Learning and Knowledge Discovery in Databases年度引用




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書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋




書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620523.jpg

破布

0302-9743 ledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019..The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. ..The contributions were organized in topical sectio

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A Framework for Parallelizing Hierarchical Clustering Methods clustering algorithm, and then we use this notion to design new scalable distributed methods with strong worst case bounds on the running time and the quality of the solutions. Finally, we show experimentally that the introduced algorithms are efficient and close to their sequential variants in practice.

ULCER

Heavy-Tailed Kernels Reveal a Finer Cluster Structure in t-SNE Visualisationsh as MNIST, single-cell RNA-sequencing data, and the HathiTrust library. We use domain knowledge to confirm that the revealed clusters are meaningful. Overall, we argue that modifying the tail heaviness of the t-SNE kernel can yield additional insight into the cluster structure of the data.

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