Material Detail

Applying Electromagnetic Field Theory Concepts to Clustering with Constraints

Applying Electromagnetic Field Theory Concepts to Clustering with Constraints

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Bled 2009. This work shows how concepts from the electromagnetic field theory can be efficiently used in clustering with constraints. The proposed framework transforms vector data into a fully connected graph, or just works straight on the given graph data. User constraints are represented by electromagnetic fields that affect the weight of the graph's edges. A clustering algorithm is then applied on the adjusted graph, using k-distinct shortest paths as the distance measure. Our framework provides better accuracy compared to MPCK-Means, SS-Kernel-KMeans and KMeans+Diagonal Metric even when very few constraints are used, significantly improves clustering performance on some datasets that other methods fail to partition successfully, and can cluster both vector and graph datasets. All these advantages are demonstrated through thorough experimental evaluation.

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.