Justine Blackmore

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| Knowledge of clusters and their relations is important in understanding high-dimensional input data with unknown distribution. Ordinary feature maps with fully connected, xed grid topology cannot properly reeect the structure of clusters in the input space|there are no cluster boundaries on the map. Incremental feature map algorithms, where nodes and(More)
Understanding high-dimensional real world data usually requires learning the structure of the data space. The structure may contain high-dimensional clusters that are related in complex ways. Methods such as merge clustering and self-organizing maps are designed to aid the visualization and interpretation of such data. However, these methods often fail to(More)
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