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» Clustering Moving Objects via Medoid Clusterings
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ICCV
2009
IEEE
15 years 20 days ago
Mode-Detection via Median-Shift
Median-shift is a mode seeking algorithm that relies on computing the median of local neighborhoods, instead of the mean. We further combine median-shift with Locality Sensitive...
Lior Shapira, Shai Avidan, Ariel Shamir
KDD
2012
ACM
247views Data Mining» more  KDD 2012»
11 years 10 months ago
Integrating meta-path selection with user-guided object clustering in heterogeneous information networks
Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is its potent...
Yizhou Sun, Brandon Norick, Jiawei Han, Xifeng Yan...
ICDE
2006
IEEE
164views Database» more  ICDE 2006»
14 years 1 months ago
Clustering Multidimensional Trajectories based on Shape and Velocity
Recently, the analysis of moving objects has become one of the most important technologies to be used in various applications such as GIS, navigation systems, and locationbased in...
Yutaka Yanagisawa, Tetsuji Satoh
SSD
2005
Springer
173views Database» more  SSD 2005»
14 years 1 months ago
On Discovering Moving Clusters in Spatio-temporal Data
A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, a convoy of cars movin...
Panos Kalnis, Nikos Mamoulis, Spiridon Bakiras
ICML
2005
IEEE
14 years 8 months ago
Multi-way distributional clustering via pairwise interactions
We present a novel unsupervised learning scheme that simultaneously clusters variables of several types (e.g., documents, words and authors) based on pairwise interactions between...
Ron Bekkerman, Ran El-Yaniv, Andrew McCallum