CURE algorithm – link to space complexity

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To avoid the problems with non-uniform sized or shaped clusters, CURE employs a [[hierarchical clustering]] algorithm that adopts a [[wikt:middle ground|middle ground]] between the centroid based and all point extremes. In CURE, a constant number c of well scattered points of a cluster are chosen and they are shrunk towards the centroid of the cluster by a fraction α. The scattered points after shrinking are used as representatives of the cluster. The clusters with the closest pair of representatives are the clusters that are merged at each step of CURE's hierarchical clustering algorithm. This enables CURE to correctly identify the clusters and makes it less sensitive to outliers.
To avoid the problems with non-uniform sized or shaped clusters, CURE employs a [[hierarchical clustering]] algorithm that adopts a [[wikt:middle ground|middle ground]] between the centroid based and all point extremes. In CURE, a constant number c of well scattered points of a cluster are chosen and they are shrunk towards the centroid of the cluster by a fraction α. The scattered points after shrinking are used as representatives of the cluster. The clusters with the closest pair of representatives are the clusters that are merged at each step of CURE's hierarchical clustering algorithm. This enables CURE to correctly identify the clusters and makes it less sensitive to outliers.


Running time is <math>O(n^2 \log n)</math>, making it rather expensive, and [[Computational complexity theory|space complexity]] is <math>O(n)</math>.
Running time is <math>O(n^2 \log n)</math>, making it rather expensive, and [[space complexity]] is <math>O(n)</math>.


The algorithm cannot be directly applied to large databases because of the high runtime complexity. Enhancements address this requirement.
The algorithm cannot be directly applied to large databases because of the high runtime complexity. Enhancements address this requirement.