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» Approximation Algorithms for Hamming Clustering Problems
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SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
15 years 9 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
70
Voted
ICML
2008
IEEE
15 years 10 months ago
ManifoldBoost: stagewise function approximation for fully-, semi- and un-supervised learning
We introduce a boosting framework to solve a classification problem with added manifold and ambient regularization costs. It allows for a natural extension of boosting into both s...
Nicolas Loeff, David A. Forsyth, Deepak Ramachandr...
TNN
2010
216views Management» more  TNN 2010»
14 years 4 months ago
Simplifying mixture models through function approximation
Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, w...
Kai Zhang, James T. Kwok
ESA
2009
Springer
167views Algorithms» more  ESA 2009»
15 years 2 months ago
Clustering-Based Bidding Languages for Sponsored Search
Sponsored search auctions provide a marketplace where advertisers can bid for millions of advertising opportunities to promote their products. The main difficulty facing the adver...
Mohammad Mahdian, Grant Wang
ICPP
2002
IEEE
15 years 2 months ago
Optimal Video Replication and Placement on a Cluster of Video-on-Demand Servers
A cost-effective approach to building up scalable Videoon-Demand (VoD) servers is to couple a number of VoD servers together in a cluster. In this article, we study a crucial vide...
Xiaobo Zhou, Cheng-Zhong Xu