Titlebook: Advances in Knowledge Discovery and Data Mining; 21st Pacific-Asia Co Jinho Kim,Kyuseok Shim,Yang-Sae Moon Conference proceedings 2017 Spri

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978-3-319-57528-5Springer International Publishing AG 2017

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Advances in Knowledge Discovery and Data Mining978-3-319-57529-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Lisa M. Diaz DO,Robert A. Norman DO, MPHn many fields of sciences, engineering, humanities and machine learning problems in general. Recent applications often encounter high dimensionality with a limited number of data points leading to a number of covariance parameters that greatly exceeds the number of observations, and hence the singul

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音樂學(xué)者

Jennifer M. Pugh BS,Porcia B. Love MDity of clustering algorithms requires user-specified parameters as input, and their clustering results rely heavily on these parameters. Second, many algorithms generate clusters of only spherical shapes. In this paper we try to solve these two problems based on dominant set and cluster expansion. W