A Hierarchical Information Theoretic Technique for the Discovery of Non Linear Alternative Clusterings

author: James Bailey, Department of Computer Science and Software Engineering, The University of Melbourne
published: Oct. 1, 2010,   recorded: July 2010,   views: 2822
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Discovery of alternative clusterings is an important method for exploring complex datasets. It provides the capability for the user to view clustering behaviour from different perspectives and thus explore new hypotheses. However, current algorithms for alternative clustering have focused mainly on linear scenarios and may not perform as desired for datasets containing clusters with non linear shapes. Our goal in this paper is to address this challenge of non linearity. In particular, we propose a novel algorithm to uncover an alternative clustering that is distinctively different from an existing, reference clustering. Our technique is information theory based and aims to ensure alternative clustering quality by maximizing the mutual information between clustering labels and data observations, whilst at the same time ensuring alternative clustering distinctiveness by minimizing the information sharing between the two clusterings. We perform experiments to assess our method against a large range of alternative clustering algorithms in the literature. We show our technique's performance is generally better for non-linear scenarios and furthermore, is highly competitive even for simpler, linear scenarios.

See Also:

Download slides icon Download slides: kdd2010_bailey_hitt_01.pdf (961.3┬áKB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: