machine-learning,cluster-analysis,similarity,unsupervised-learning

First, compute your profiles as you already did. Then the crucial step will be some kind of normalization. You can either divide the numbers by their total so that the numbers sum up to 1, or you can divide them by their Euclidean norm so that they have Euclidean norm...

security,machine-learning,cryptography,unsupervised-learning

Yes, you can distinguish some ciphers based on their ciphertexts, but this doesn't work for all modes of operation. The key observation is that AES and Triple DES have different block sizes of 128 bit and 64 bit. Which means that a 7 byte message will be 8 bytes long...

artificial-intelligence,neural-network,unsupervised-learning,deep-learning

I don't see why you are using unsupervised learning. It sounds like a purely supervised learning task. You shouldn't throw away data for predicting rare events. If an event is very rare than of course the network will predict it has a very low probability. Because it does. This is...

machine-learning,cluster-analysis,k-means,unsupervised-learning

The k-means algorithm requires some initialization of the centroid positions. For most algorithms, these centroids are randomly initialized with some method such as the Forgy method or random partitioning, which means that repeated iterations of the algorithm can converge to vastly different results. Remember that k-means is iterative, and at...

r,machine-learning,cluster-analysis,som,unsupervised-learning

Map 1 is the average vector result for each node. The top 2 nodes that you highlighted are very similar. Map 2 is a kind of similarity index between the nodes. If you want to obtain such kind of map using the map 1 result you may have to develop...