Comments (5)
I haven't compared my implementation to sklearn's, so I'm not sure.
A huge potential difference between the two is that sklearn uses the powerful k-means++ initialization method by default. Are the results still very different when you use init='random'
in sklearn?
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I am in the process of confirming that. Just to make sure, the catigorical part will not have any impact when gamma is set to 0?
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Correct
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Just for curiosity, what is the range of gamma ?
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@sharma-ji , from 0 (i.e. you're effectively doing k-means) to infinite-ish (i.e. you're effectively doing k-modes).
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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
- What are the minimum characteristics that a binary matrix must meet to avoid the following error: "Insufficient Number of data since union is 0"? 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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