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Use a set of representative points to find non-global clusters

- these points capture the geometry and shape of clusters

**TODO: see Scalable Data Analytics and Data Mining AIM3 (TUB) lectures **

Choose points

- 2 farthest away points
- 3 and so on - furthest away from previous ones
- this procedure guarantees that the points are well distributed

Then shrink the points towards the centroids by factor of $\alpha$

CURE eliminates outliers by discarding small slowly growing clusters

- but it has a notion of center - not all shapes has natural center

- Guha, Sudipto, Rajeev Rastogi, and Kyuseok Shim. "Cure: an efficient clustering algorithm for large databases." (2001) [1]

- Ertöz, Levent, Michael Steinbach, and Vipin Kumar. "Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data." 2003. [2]
- http://en.wikipedia.org/wiki/CURE_data_clustering_algorithm