504 search results - page 56 / 101 » Clustering Item Data Sets with Association-Taxonomy Similari... |
PKDD
2010
Springer
13 years 8 months ago
2010
Springer
The goal of graph clustering is to partition objects in a graph database into different clusters based on various criteria such as vertex connectivity, neighborhood similarity or t...
ISAAC
2009
Springer
14 years 5 months ago
2009
Springer
The k-means algorithm is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice. Most of the theoretical work is restricted to the c...
CVPR
2007
IEEE
15 years 26 days ago
2007
IEEE
We propose a manifold learning approach to fiber tract clustering using a novel similarity measure between fiber tracts constructed from dual-rooted graphs. In particular, to gene...
CISS
2008
IEEE
2008
IEEE
A lower-bound on the number of rankings required in recommender systems using collaborativ filtering
14 years 5 months ago
— We consider the situation where users rank items from a given set, and each user ranks only a (small) subset of all items. We assume that users can be classified into C classe...
ALMOB
2006
13 years 11 months ago
2006
Given a set S of n locally aligned sequences, it is a needed prerequisite to partition it into groups of very similar sequences to facilitate subsequent computations, such as the ...