clustering | Clustering algorithms in Ruby

I have two data frames. df1 and df2. both with c columns
using a clustering method, I ended up with 10 clusters. same clusters for each df is true. this means for example the 4th row of both df s go to the same cluster.
I added a cluster column to both dfs, showing the assigned cluster for each row.

I want to create a list.
this list contains 10 matrices, such that.
matrix 1, is a 2*c matrix. its first row is obtained by colmeans of those rows of df1 which are in cluster 1. and its 2nd row is obtained by colmeans of those rows of df2 which are in cluster 1.
and matrix 2 , colmeans of cluster 2 and so on.
this is what I ve done. but I get the 10th matrix only and not a list of matrices 1 to 10.
I would appreciate any help with this.

I'm using Tomcat 10 and eclipse to develop a J2E (or Jakarta EE) web application. I followed this tutorial (http://objis.com/tutoriel-securite-declarative-jee-avec-jaas/#partie2) which seems old (it's a french document, because i'm french, sorry if my english isn't perfect), but I also read the Tomcat 10 documentation.
The dataSource works, I followed instructions on this page (https://tomcat.apache.org/tomcat-10.0-doc/jndi-datasource-examples-howto.html#Oracle_8i,_9i_&_10g) and tested it, but it seems that the realm doesn't work, because I can't login successfully. I always have an authentification error, even if I use the right login and password.
I tried a lot of "solutions" to correct this, but no one works. And I still don't know if I have to put the realm tag inside context.xml, server.xml or both. I tried context.xml and both, but i don't see any difference.

My web.xml :

I have a Google BigQuery clustered partition table. And I am trying to get Google BigQuery clustered partition table definition using bq.py cli tool. I get the json output but it does not have clustering information.

How can I mask the lower triangle while hierarchical clustering with seaborn's clustermap?

I am trying to learn the k-means clustering algorithm in MATLAB without using inbuilt k-means function. Say I have the data of size 1x100 and I want to group them into two clusters. So how can I do this. I want to visualize the two centroids and data together on a plot in MATLAB. Note : When I plot in MATLAB, I am able to see only data but not the data and two centroids simultaneously.

Any help in this regard is highly appreciated.

• I'm working on the NLP dataset.
• I Have a data-set which I want to train on classification problem (with 5 classes) and after this phase, I want to use the trained model on the test data in order to build embedding vectors which will be used for the clustering algorithm.

I built the following model:

I'm working on Kmeans clustering but unlike supervised learning I cannot figure the performance metrics for clustering algorithms. How to perform the accuracy after training the data?

For image clustering I was using a piece of code which worked perfectly.

I have a 4 column dataframe which I extracted from the iris dataset. I use kmeans to plot 3 clusters from all possible combinations of 2 columns.

However, there seems to be something wrong with the output, especially since the cluster centers are not placed at the center of the clusters. I have provided examples of the output. Only cluster_1 seems OK but the other 3 look completely wrong .

How best can I fix my clustering? This is the sample code I am using