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SDM
2004
SIAM
162views Data Mining» more  SDM 2004»
15 years 7 months ago
Subspace Clustering of High Dimensional Data
Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that...
Carlotta Domeniconi, Dimitris Papadopoulos, Dimitr...
ICDM
2008
IEEE
143views Data Mining» more  ICDM 2008»
16 years 23 days ago
Exploiting Data Semantics to Discover, Extract, and Model Web Sources
We describe DEIMOS, a system that automatically discovers and models new sources of information. The system exploits four core technologies developed by our group that makes an en...
José Luis Ambite, Craig A. Knoblock, Kristi...
ICDM
2007
IEEE
139views Data Mining» more  ICDM 2007»
16 years 19 days ago
Data Clustering with a Relational Push-Pull Model
We present a new generative model for relational data in which relations between objects can have either a binding or a separating effect. For example, in a group of students sep...
Adam Anthony, Marie desJardins
ICDM
2002
IEEE
191views Data Mining» more  ICDM 2002»
15 years 11 months ago
Iterative Clustering of High Dimensional Text Data Augmented by Local Search
The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However, spherical k-means ca...
Inderjit S. Dhillon, Yuqiang Guan, J. Kogan
PAKDD
2005
ACM
112views Data Mining» more  PAKDD 2005»
15 years 11 months ago
Approximated Clustering of Distributed High-Dimensional Data
In many modern application ranges high-dimensional feature vectors are used to model complex real-world objects. Often these objects reside on different local sites. In this paper,...
Hans-Peter Kriegel, Peter Kunath, Martin Pfeifle, ...