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ID | Category | Severity | Reproducibility | Date Submitted | Last Update | |||||||
0000472 | [ALGLIB] Data analysis | feature | have not tried | 2012-07-12 19:10 | 2018-01-02 17:22 | |||||||
Reporter | SergeyB | View Status | public | |||||||||
Assigned To | SergeyB | |||||||||||
Priority | normal | Resolution | open | |||||||||
Status | assigned | Product Version | ||||||||||
Summary | 0000472: Cluster analysis improvements | |||||||||||
Description |
SSE-based distance matrix calculation for L1 and L-inf norms. Sparse input matrices RNN algorithm and other improvements: * http://www.daimi.au.dk/~zxr/papers/quadtreeUPGMA.pdf * http://thames.cs.rhul.ac.uk/~fionn/old-articles/complexities/ Divisive clustering (can be easily parallelized). Better k-means: * smart iteration: http://www.siam.org/proceedings/datamining/2010/dm10_012_hamerlyg.pdf Penalized k-means and penalized-weighted kmeans? * George C. Tseng, articles Different clustering techniques from http://en.wikipedia.org/wiki/Cluster_analysis : * EM and Gaussian * DBSCAN and OPTICS * hard vs. soft * other exotic varietes * coefficients Different cluster validation coefficients: http://scikit-learn.org/stable/modules/clustering.html http://www.cs.kent.edu/~jin/DM08/ClusterValidation.pdf http://web.itu.edu.tr/sgunduz/courses/verimaden/paper/validity_survey.pdf |
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Additional Information | ||||||||||||
Programming language | Unspecified | |||||||||||
Attached Files | ||||||||||||
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