A2 – Clustering

In assignment 2 you shall implement clustering on the blogs dataset in any programming language you like. You can work alone or in group of two students. You shall present your application and code at an oral examination.

Grade Requirements
E
  • Implement K-means Clustering with Pearson similarity
  • Run the algorithm on the blog data dataset (see Datasets page) with 5 clusters
  • Present the result as a list of clusters and their assignments in a web application
    using a RESTful web service as back-end, and a browser client GUI as front-end
  • The iteration shall stop after a specified number of iterations
C-D
  • 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
A-B
  • Implement Hierarchical Clustering with Pearson similarity
  • Run the algorithm on the blog data dataset
  • Present the result as an interactive tree in the web application (it shall be possible to expand/collapse branches)

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