Comments (1)
This appears to occur due to the jaccard_dissim_binary
dissimilarity function, in combination with some specifics of the data set. Specifically, it appears this distance metric does not support situations where you have binary data, and all rows have the same value for a column.
You'll either need to:
- Add some more variety to your data.
- Use a different dissimilarity metric.
- Drop constant columns from the data beforehand. Since there's no point in differentiating between such columns, you may as well. That way, you can still use this distance function.
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Related Issues (20)
- k-prototype seems to focus on one continuous variable HOT 1
- Reduce memory usage in array initialization HOT 2
- GPU ( cuda ) support? HOT 1
- Add L1 as a dissimilarity function option for continuous variables HOT 1
- Performance over binary data HOT 1
- parallelization HOT 4
- KPrototypes fit_predict fails with sample_weight HOT 2
- Apologies if this is redundant but I could not find documentation ... how do you extract class membership from an object created by the function KPrototypes HOT 1
- ValueError: All arrays must be of the same length HOT 3
- Euclidean distance definiton lacks a square root HOT 2
- Support Arm64 macos HOT 1
- Please add conda installation information HOT 1
- Different clusters when K-Prototypes trained on same data in numpy array and pandas dataframe HOT 1
- Li
- Estimation of Gamma in K-Prototypes HOT 1
- [BUG] Badge not rendering in readme HOT 2
- Incorrect dtype conversion of categoricals when dealing with manually assigned centroids HOT 2
- Create equal-sized clusters within kmodes HOT 1
- Value Error when I pass a NumPy array as init parameter HOT 1
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