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» Clustering Moving Objects via Medoid Clusterings
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ICDE
2008
IEEE
219views Database» more  ICDE 2008»
14 years 9 months ago
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the k-anonymity principle, each release of data must be such th...
Osman Abul, Francesco Bonchi, Mirco Nanni
HOTOS
2007
IEEE
13 years 11 months ago
Hyperspaces for Object Clustering and Approximate Matching in Peer-to-Peer Overlays
Existing distributed hash tables provide efficient mechanisms for storing and retrieving a data item based on an exact key, but are unsuitable when the search key is similar, but ...
Bernard Wong, Ymir Vigfusson, Emin Gün Sirer
ICDM
2002
IEEE
191views Data Mining» more  ICDM 2002»
14 years 19 days 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
ICIP
2007
IEEE
14 years 9 months ago
Unsupervised Modeling of Object Tracks for Fast Anomaly Detection
A key goal of far-field activity analysis is to learn the usual pattern of activity in a scene and to detect statistically anomalous behavior. We propose a method for unsupervised...
Tomas Izo, W. Eric L. Grimson
VIP
2003
13 years 9 months ago
Robust Real-Time Tracking of Non-Rigid Objects
Several approaches to real-time video object tracking are reviewed. A new alternative approach for fast real-time object tracking based on colour thresholding is presented. Tracki...
Richard Y. D. Xu, John G. Allen, Jesse S. Jin