sim3: Synthetic dataset used in section 5.1.3 of the reference...
In HDclust: Clustering High Dimensional Data with Hidden Markov Model on Variable Blocks
Description
Usage
Format
References
Description
Dataset used for testing clustering with HMM-VB.
The data dimension is 40. The first 10 dimensions were generated from a 3-component Gaussian
Mixture Model (GMM). The remaining 30 dimensions were generated from a 5-component GMM. By specific design
of the means, covariance matrices and transition probabilities, the data contain 5 distinct clusters.
For details see the references.
Usage
1
Format
A data frame with 1000 rows and 40 variables. Last column contains ground truth cluster labels.
References
Lin Lin and Jia Li, "Clustering with hidden Markov model on variable blocks," Journal of Machine Learning Research, 18(110):1-49, 2017.
HDclust documentation built on May 2, 2019, 9:20 a.m.
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Description Usage Format References
Description
Dataset used for testing clustering with HMM-VB. The data dimension is 40. The first 10 dimensions were generated from a 3-component Gaussian Mixture Model (GMM). The remaining 30 dimensions were generated from a 5-component GMM. By specific design of the means, covariance matrices and transition probabilities, the data contain 5 distinct clusters. For details see the references.
Usage
1 |
Format
A data frame with 1000 rows and 40 variables. Last column contains ground truth cluster labels.
References
Lin Lin and Jia Li, "Clustering with hidden Markov model on variable blocks," Journal of Machine Learning Research, 18(110):1-49, 2017.
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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