Linked Life Data

11958483

Source:http://linkedlifedata.com/resource/pubmed/id/11958483

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2002-4-17
pubmed:abstractText
A new neural network architecture (PART) and the resulting algorithm are proposed to find projected clusters for data sets in high dimensional spaces. The architecture is based on the well known ART developed by Carpenter and Grossberg, and a major modification (selective output signaling) is provided in order to deal with the inherent sparsity in the full space of the data points from many data-mining applications. This selective output signaling mechanism allows the signal generated in a node in the input layer to be transmitted to a node in the clustering layer only when the signal is similar to the top-down weight between the two nodes and, hence, PART focuses on dimensions where information can be found. Illustrative examples are provided, simulations on high dimensional synthetic data and comparisons with Fuzzy ART module and PROCLUS are also reported.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0893-6080
pubmed:author
pubmed:issnType
Print
pubmed:volume
15
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
105-20
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Projective ART for clustering data sets in high dimensional spaces.
pubmed:affiliation
Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't