BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:High-Dimensional Incremental Divisive Clustering under Population 
 Drift - Pavlidis\, N (Lancaster University)
DTSTART:20140115T153000Z
DTEND:20140115T160000Z
UID:TALK49921@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION: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 foc
 used on addressing individual aspects of the problem in isolation\, largel
 y ignoring whether and how the proposed methods can be extended to address
  the overall problem. We will discuss an incremental divisive clustering a
 pproach for high-dimensional data that has storage requirements that are l
 ow and more importantly independent of the stream size\, and can identify 
 changes in the population distribution that require a revision of the clus
 tering result.\n
LOCATION:Seminar Room 1\, Newton Institute
END:VEVENT
END:VCALENDAR