lecture12-clustering-handout-6-per

Next update the seeds to the centroid of each cluster

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: d K ­ medoids algorithms See also Kleinberg NIPS 2002 – impossibility for natural clustering Introduc)on to Informa)on Retrieval Sec. 16.4 K ­Means   Assumes documents are real ­valued vectors.   Clusters based on centroids (aka the center of gravity or mean) of points in a cluster, c: 1 µ(c) = ∑x | c | x∈c   Reassignment of instances to clusters is based on distance to the current cluster centroids. Introduc)on to Informa)on Retrieval Sec. 16.4 K ­Means Algorithm Select K random docs {s1, s2,… sK} as seeds. Un*l clustering converges (or other stopping criterion): For each doc di: Assign di to the cluster cj such that dist(xi, sj) is minimal. (Next, update the seeds to the centroid of each cluster) For each cluster cj sj = µ(cj)   (Or one can equivalently phrase it in t...
View Full Document

This document was uploaded on 02/26/2014.

Ask a homework question - tutors are online