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VLDB
2007
ACM
174views Database» more  VLDB 2007»
14 years 7 months ago
An adaptive and dynamic dimensionality reduction method for high-dimensional indexing
Abstract The notorious "dimensionality curse" is a wellknown phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approa...
Heng Tao Shen, Xiaofang Zhou, Aoying Zhou
HAIS
2009
Springer
14 years 8 days ago
Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty
Developing models and methods to manage data vagueness is a current effervescent research field. Some work has been done with supervised problems but unsupervised problems and unce...
José Ramón Villar, María del ...
DATESO
2010
148views Database» more  DATESO 2010»
13 years 5 months ago
Using Spectral Clustering for Finding Students' Patterns of Behavior in Social Networks
Abstract. The high dimensionality of the data generated by social networks has been a big challenge for researchers. In order to solve the problems associated with this phenomenon,...
Gamila Obadi, Pavla Drázdilová, Jan ...
KDD
2000
ACM
149views Data Mining» more  KDD 2000»
13 years 11 months ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar
DEXA
2006
Springer
190views Database» more  DEXA 2006»
13 years 11 months ago
High-Dimensional Similarity Search Using Data-Sensitive Space Partitioning
Abstract. Nearest neighbor search has a wide variety of applications. Unfortunately, the majority of search methods do not scale well with dimensionality. Recent efforts have been ...
Sachin Kulkarni, Ratko Orlandic