Brown Bag Seminar - April 26, 2006
Speaker: Cem Iyigun
Affiliation: RUTCOR, Rutgers University
Title: Probabilistic Distance Clustering: Algorithm and Applications.
Time: 1:30 - 2:30 PM
Location: RUTCOR Building - Lounge, Rutgers University, Busch Campus, Piscataway, NJ
Abstract:
Clustering is a process of partitioning a data set into clusters, i.e.
subsets of data points that are similar in some sense. Probabilistic
clustering is when cluster membership is expressed by probabilities
p(x|C) that a point x belongs to a cluster C. Distance clustering is
when similar means close w.r.t. a given distance function (Euclidean,
Mahalanobis, etc.)
I present new approach and method for probabilistic clustering of
data. Given clusters, their sizes (except if these are unknown and
have to be estimated), centers, and the distances of data points from
these centers, the probability of cluster membership at any point is
assumed to be inversely proportional to its distance from the center
of that cluster, and directly proportional to the cluster size. The
method is based on the above assumption, and on the joint distance
function, a weighted harmonic mean of distance from all cluster
centers, that evolves during the iterations, and captures the data in
its low contours. The method is simple, and works well and fast.
In addition to clustering, I present application to location of
several capacitated facilities, and to demixing of mixtures of
distributions, where the proposed method is a viable alternative to
the EM method for estimating the relevant parameters.
Joint work with Adi Ben-Israel, Rutgers University.
Back to Seminars Page.
Back to RUTCOR homepage.
| 
