My solution for assignment 2 in Web Intelligence (2DV515) at Linnaeus University

• Implement K-means Clustering with Pearson similarity
• Run the algorithm on the blog data dataset (see Datasets page) with 5 clusters
• The iteration shall stop after a specified number of iterations
• Present the result as a list of clusters and their assignments
• Implement the system using a REST web service where:
  • client sends a request to a server
  • the server responds with json data
  • the json data is decoded and presented in a client GUI

• Instead of stopping after a specified number of iterations, you shall implement functionality for stopping when no new assignments are made
• Each cluster must keep track of the previous assignment, and a check is made if the new cluster assignment matches the previous one

• Implement Hierarchical Clustering with Pearson similarity
• Run the algorithm on the blog data dataset
• Present the result as an interactive tree in the client GUI (it shall be possible to expand/collapse branches)

Installs all dependencies for both client and server and generates a new data.json.

Starts both client (localhost:3000) and server (localhost:3001).

Start only client on localhost:3000.

Start only server on localhost:3001.