Answers.Body: <p>This is called "clustering" , If the network is already trained with data that has similar features as of the "symbols", you can use that network with its last classification layer removed , then run a clustering algorithm like "k-means" on top of the vectors obtained from the last layer of the network.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-07-05T03:44:06.693Z
Answers.LastActivityDate: 2018-07-05T03:44:06.693Z
Answers.UserDisplayName: thecomplexitytheorist
Body: <p>I'm a complete newbie to NNs, and I need your advice.</p>
  <p>I have a set of images of symbols, and my goal is to categorize and divide them into groups of symbols that look alike. Without teaching NN anything about the data.</p>
  <p>What is the best way to do this? What type of NN suits the best? Maybe there are any ready solutions?</p>
  
Tags: <neural-networks><unsupervised-learning><algorithm-request><unlabeled-datasets>
Title: Given a set of images that are not divided into groups, which algorithm should I use to do that?
UserDisplayName: user2758776
ThreadText: # Given a set of images that are not divided into groups, which algorithm should I use to do that?
  
  (Asked by: username_0 on 2018-07-04)
  
  I'm a complete newbie to NNs, and I need your advice.
  
  I have a set of images of symbols, and my goal is to categorize and divide them into groups of symbols that look alike. Without teaching NN anything about the data.
  
  What is the best way to do this? What type of NN suits the best? Maybe there are any ready solutions?
  
  **Tags:** neural-networks, unsupervised-learning, algorithm-request, unlabeled-datasets
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2018-07-05. Score: 2 | Accepted answer
  
  This is called "clustering" , If the network is already trained with data that has similar features as of the "symbols", you can use that network with its last classification layer removed , then run a clustering algorithm like "k-means" on top of the vectors obtained from the last layer of the network.
  
  
  ---
  
  