Here is nice implementation of mixed type data in R-
This question right here-
K-Means clustering for mixed numeric and categorical data
and a Discussion Thread of Kaggle-
There are ways, to either map your categorical data to numeric type and then you can go ...
K-Means is a clustering technique NOT classification. You don't have the ground truth here to compare with. Hence accuracy doesn't make any sense.
You can train the model and with the test data predict which cluster the test data belongs to.
Try to visualize it, it shall be helpful.
This is a method for evaluation of two clusterings in the presence of class labels so it is not proper for real clustering problems in which class labels are not available.
Imagine you have class labels and you want to evaluate a clustering or (compare two clusterings). The most natural idea is to use Purity score. It simply checks labels with clusters and ...
There are different ways to address the task that you describe:
If the goal is simply to predict the author among a set of predefined authors, then this is not a clustering task (unsupervised) but a classification task (supervised). This implies that you would split the data between training and test set (or use cross-validation), train a model using the ...
I was also looking for something similar and found a couple of libraries (https://matrixprofile.org/libraries/) that might work but I haven’t tried them out much yet.
I hope that helps and if anyone has any other suggestions I’d be interested as well.
You will need some way of converting categorical data to numerical, or numerical to categorical. One way to do this (convert categorical to numerical) is with one-hot encoding, where you look at the number of categories you have and make a vector of that size. Then, you can map each datapoint to a vector with 0 everywhere except for the location for the ...