Clustering Paolo Ferragina Dipartimento di Informatica Università di Pisa This is a mix of slides taken from several presentations, plus my touch !
CSCI 5417 Information Retrieval Systems Jim Martin Lecture 16 10/18/2011.
CS728 Web Clustering II Lecture 14. K-Means Assumes documents are real-valued vectors. Clusters based on centroids (aka the center of gravity or mean)
Flat Clustering
Clustering Supervised vs. Unsupervised Learning Examples of clustering in Web IR Characteristics of clustering Clustering algorithms Cluster Labeling 1.
Basic Machine Learning: Clustering CS 315 – Web Search and Data Mining 1.
Clustering
CSCI 5417 Information Retrieval Systems Jim Martin Lecture 15 10/13/2011.
Today’s Topic: Clustering Document clustering Motivations Document representations Success criteria Clustering algorithms Partitional Hierarchical.
CS276
CSC 4510 – Machine Learning
