D31: The Artificial Data of Consisting of as Many as 31 Randomly...
In supc: The Self-Updating Process Clustering Algorithms
Description
This artificial data was generated to show the strength of SUPC.
Clustering D31 dataset is difficult for the partition type of clustering algorithms that require an initial set.
However, SUP correctly identifies the 31 major clusters.
References
Veenman, C. J., M. J. T. Reinders, and E. Backer. 2002. A Maximum Variance Cluster Algorithm. IEEE Trans. Pattern Analysis and Machine Intelligence 24 (9): 1273–80.
supc documentation built on Dec. 11, 2021, 5:07 p.m.
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Description
This artificial data was generated to show the strength of SUPC.
Clustering D31 dataset is difficult for the partition type of clustering algorithms that require an initial set.
However, SUP correctly identifies the 31 major clusters.
References
Veenman, C. J., M. J. T. Reinders, and E. Backer. 2002. A Maximum Variance Cluster Algorithm. IEEE Trans. Pattern Analysis and Machine Intelligence 24 (9): 1273–80.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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