Probability and Statistics for Computer Science pp 281-304 | Cite as

# Clustering: Models of High Dimensional Data

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## Abstract

High-dimensional data comes with problems. Data points tend not to be where you think; they can scattered quite far apart, and can be quite far from the mean. There is an important rule of thumb for coping with high dimensional data: **Use simple models**. One very good, very simple, model for high dimensional data is to assume that it consists of multiple blobs. To build models like this, we must determine which datapoints belong to which blob by collecting together data points that are close and forming blobs out of them.

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