K-means- | 使用pandas 、numpy 、K-means算法、matplotlib分析航空公司客户价值 | Machine Learning library

I've been trying to run RAPIDS on Google Colab pro, and have successfully installed the cuml and cudf packages, however I am unable to run even the example scripts.

Anytime I try to run the fit function for cuml on Google Colab I get the following error. I get this when using the demo examples both for installation and then for cuml. This happens for a range of cuml examples (I first hit this trying to run UMAP).

I want to cluster pdf documents based on their structure, not only the text content.

The main problem with the text only approach is, that it will loose the information if a document has a pdf form structure or was it just a plain doc or does it contain pictures?

For our further processing these information are most important. My main goal is now to be able to classify a document regarding mainly its structure not only the text content.

The documents to classify are stored in a SQL database as byte[] (varbinary), so my idea is now to use the this raw data for classification, without prior text conversion.

Because if I look at the hex output of these data, I can see repeating structures which seems to be similar to the different doc classes I want to separate. You can see some similar byte patterns as first impression in my attached screenshot.

So my idea is now to train a K-Means model with e.g. a hex output string. In the next step I would try to find the best number of clusters with the elbow method, which should be around 350 - 500.

The size of the pdf data varies between 20 kByte and 5 MB, mostly around 150 kBytes. To train the model I have +30.k documents.

When I research that, the results are sparse. I only find this article, which make me unsure about the best way to solve my task. https://www.ibm.com/support/pages/clustering-binary-data-k-means-should-be-avoided

My questions are:

• Is K-Means the best algorithm for my goal?
• What method do you would recommend?
• How to normalize or transform the data for the best results?

I am performing a binary classification of a partially labeled dataset. I have a reliable estimate of its 1's, but not of its 0's.

From sklearn KMeans documentation:

I'm trying to reduce the input data size by first performing a K-means clustering in R then sample 50-100 samples per representative cluster for downstream classification and feature selection.

The original dataset was split 80/20, and then 80% went into K means training. I know the input data has 2 columns of labels and 110 columns of numeric variables. From the label column, I know there are 7 different drug treatments. In parallel, I tested the elbow method to find the optimal K for the cluster number, it is around 8. So I picked 10, to have more data clusters to sample for downstream.

Now I have finished running the model <- Kmeans(), the output list got me a little confused of what to do. Since I have to scale only the numeric variables to put into the kmeans function, the output cluster membership don't have that treatment labels anymore. This I can overcome by appending the cluster membership to the original training data table.

Then for the 10 centroids, how do I find out what the labels are? I can't just do

I am using a k-modes model (mymodel) which is created by a data frame mydf1. I am looking to assign the nearest cluster of mymodel for each row of a new data frame mydf2. Similar to this question - just with k-modes instead of k-means. The predict function of the flexclust package only works with numeric data, not categorial.

A short example:

I am new to machine learning and i am using

This question will be a duplicate unfortunately, but I could not fix the issue in my code, even after looking at the other similar questions and their related answers. I need to split my dataset into train a test a dataset. However, it seems I am doing some error when I add a new column for predicting the cluster. The error that I get is:

I'm trying to learn sklearn. As I understand from step 5 of the following example, the predicted clusters can be mislabelled and it would be up to me to relabel them properly. This is also done in an example on sci-kit. Labels must be re-assigned so that the results of the clustering and the ground truth match by color.

How would I know if the labels of the predicted clusters match the initial data labels and how to readjust the indices of the labels to properly match the two sets?

I am testing this code.