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.