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» On k-Anonymity and the Curse of Dimensionality
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VLDB
2005
ACM
136views Database» more  VLDB 2005»
14 years 4 months ago
On k-Anonymity and the Curse of Dimensionality
In recent years, the wide availability of personal data has made the problem of privacy preserving data mining an important one. A number of methods have recently been proposed fo...
Charu C. Aggarwal
JMLR
2010
119views more  JMLR 2010»
13 years 5 months ago
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
Different aspects of the curse of dimensionality are known to present serious challenges to various machine-learning methods and tasks. This paper explores a new aspect of the dim...
Milos Radovanovic, Alexandros Nanopoulos, Mirjana ...
VLDB
1999
ACM
224views Database» more  VLDB 1999»
14 years 3 months ago
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work e ectively and e ciently in highdimensional sp...
Alexander Hinneburg, Daniel A. Keim
SDM
2012
SIAM
261views Data Mining» more  SDM 2012»
12 years 1 months ago
Combining Active Learning and Dynamic Dimensionality Reduction
To date, many active learning techniques have been developed for acquiring labels when training data is limited. However, an important aspect of the problem has often been neglect...
Mustafa Bilgic
JMLR
2010
153views more  JMLR 2010»
13 years 5 months ago
Feature Extraction for Outlier Detection in High-Dimensional Spaces
This work addresses the problem of feature extraction for boosting the performance of outlier detectors in high-dimensional spaces. Recent years have observed the prominence of mu...
Nguyen Hoang Vu, Vivekanand Gopalkrishnan