Drop support for missing values, following sklearn

README.rst

@@ -98,29 +98,6 @@ Usage
 The examples directory showcases simple use cases of both k-modes
 ('soybean.py') and k-prototypes ('stocks.py').
 
-Missing / unseen data
-_____________________
-
-The k-modes algorithm accepts :code:`np.NaN` values as missing values in
-the :code:`X` matrix. However, users are strongly suggested to consider
-filling in the missing data themselves in a way that makes sense for
-the problem at hand. This is especially important in case of many missing
-values.
-
-The k-modes algorithm currently handles missing data as follows. When
-fitting the model, :code:`np.NaN` values are encoded into their own
-category (let's call it "unknown values"). When predicting, the model
-treats any values in :code:`X` that (1) it has not seen before during
-training, or (2) are missing, as being a member of the "unknown values"
-category. Simply put, the algorithm treats any missing / unseen data as
-matching with each other but mismatching with non-missing / seen data
-when determining similarity between points.
-
-The k-prototypes also accepts :code:`np.NaN` values as missing values for
-the categorical variables, but does *not* accept missing values for the
-numerical values. It is up to the user to come up with a way of
-handling these missing data that is appropriate for the problem at hand.
-
 Parallel execution
 ------------------
 
@@ -171,6 +148,14 @@ A: This is a feature, not a bug. :code:`kmodes` is telling you that it can't mak
 
 ----
 
+**Q: I'm getting the following error: "ValueError: Input contains NaN, infinity, or a value too large for dtype('float64')."**
+
+A: Following scikit-learn, the k-modes algorithm does not accept :code:`np.NaN` 
+values in the :code:`X` matrix. Users are suggested to fill in the missing 
+data in a way that makes sense for the problem at hand.
+
+----
+
 **Q: How would like your library to be cited?**
 
 A: Something along these lines would do nicely: