JASIS
2007
13 years 4 months ago
2007
A distributed memory parallel version of the group average Hierarchical Agglomerative Clustering algorithm is proposed to enable scaling the document clustering problem to large c...
PRL
2006
13 years 4 months ago
2006
This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocatio...
PRL
2008
13 years 4 months ago
2008
We propose a novel hierarchical clustering algorithm for data-sets in which only pairwise distances between the points are provided. The classical Hungarian method is an efficient...
KAIS
2006
13 years 4 months ago
2006
Abstract. Data mining in large databases of complex objects from scientific, engineering or multimedia applications is getting more and more important. In many areas, complex dista...
DATAMINE
2006
13 years 4 months ago
2006
With the growing importance of time series clustering research, particularly for similarity searches amongst long time series such as those arising in medicine or finance, it is cr...
BMCBI
2006
13 years 4 months ago
2006
Background: Cluster analysis is an integral part of high dimensional data analysis. In the context of large scale gene expression data, a filtered set of genes are grouped togethe...
BMCBI
2007
13 years 4 months ago
2007
Background: Data clustering analysis has been extensively applied to extract information from gene expression profiles obtained with DNA microarrays. To this aim, existing cluster...
CLEF
2010
Springer
13 years 5 months ago
2010
Springer
In the third edition of WePS campaign we have undertaken the person name disambiguation problem referred to as a clustering task. Our aim was to make use of intrinsic link relation...
ACL
2000
13 years 6 months ago
2000
We present a clustering algorithm for Arabic words sharing the same root. Root based clusters can substitute dictionaries in indexing for IR. Modifying Adamson and Boreham (1974),...
NIPS
2004
13 years 6 months ago
2004
An important aspect of clustering algorithms is whether the partitions constructed on finite samples converge to a useful clustering of the whole data space as the sample size inc...