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- W4299566753 abstract "Cluster analysis of very high dimensional data can benefit from the properties of such high dimensionality. Informally expressed, in this work, our focus is on the analogous situation when the dimensionality is moderate to small, relative to a massively sized set of observations. Mathematically expressed, these are the dual spaces of observations and attributes. The point cloud of observations is in attribute space, and the point cloud of attributes is in observation space. In this paper, we begin by summarizing various perspectives related to methodologies that are used in multivariate analytics. We draw on these to establish an efficient clustering processing pipeline, both partitioning and hierarchical clustering." @default.
- W4299566753 created "2022-10-02" @default.
- W4299566753 creator A5000885553 @default.
- W4299566753 date "2017-04-06" @default.
- W4299566753 modified "2023-09-26" @default.
- W4299566753 title "Massive Data Clustering in Moderate Dimensions from the Dual Spaces of Observation and Attribute Data Clouds" @default.
- W4299566753 doi "https://doi.org/10.48550/arxiv.1704.01871" @default.
- W4299566753 hasPublicationYear "2017" @default.
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