skip to content
 

High-Dimensional Incremental Divisive Clustering under Population Drift

Presented by: 
N Pavlidis Lancaster University
Date: 
Wednesday 15th January 2014 - 15:30 to 16:00
Venue: 
INI Seminar Room 1
Abstract: 
Clustering is a central problem in data mining and statistical pattern recognition with a long and rich history. The advent of Big Data has introduced important challenges to existing clustering methods in the form of high-dimensional, high-frequency, time-varying streams of data. Up-to-date research on Big Data clustering has been almost exclusively focused on addressing individual aspects of the problem in isolation, largely ignoring whether and how the proposed methods can be extended to address the overall problem. We will discuss an incremental divisive clustering approach for high-dimensional data that has storage requirements that are low and more importantly independent of the stream size, and can identify changes in the population distribution that require a revision of the clustering result.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons