CURE algorithm
CURE (Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases. Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances.
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- enCURE (Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases. Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances.
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- enCURE (Clustering Using REpresentatives) is an efficient data clustering algorithm for large databases. Compared with K-means clustering it is more robust to outliers and able to identify clusters having non-spherical shapes and size variances.
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- CURE algorithm
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- enCURE algorithm
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- books.google.com/books%3Fid=gAGRCmp8Sp8C&pg=PA572%7Cpages=572%E2%80%93574%7Cauthor2=Koutroumbas,
- www.cs.sfu.ca/CC/459/han/papers/guha98.pdf%7Ctitle=
- github.com/annoviko/pyclustering
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- Analysis of algorithms
- BFR algorithm
- BIRCH (data clustering)
- Category:Articles with example pseudocode
- Category:Cluster analysis algorithms
- Computational complexity theory
- Database
- Data clustering
- Data point
- Hierarchical clustering
- Kd-tree
- K-means clustering
- Outlier
- Primary storage
- Random sample
- Robust statistics
- Sample space
- Sampling (statistics)
- Sum of squared error
- Trade-off
- Wikt:middle ground
- SameAs
- 4eZjo
- Q5014717
- Алгоритм CURE
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- Category:Articles with example pseudocode
- Category:Cluster analysis algorithms
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- CURE algorithm?oldid=1085332370&ns=0
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- 1085332370
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