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» Approximation Algorithms for Clustering Problems
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KDD
2004
ACM
158views Data Mining» more  KDD 2004»
16 years 3 days ago
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Co-clustering is a powerful data mining technique with varied applications such as text clustering, microarray analysis and recommender systems. Recently, an informationtheoretic ...
Arindam Banerjee, Inderjit S. Dhillon, Joydeep Gho...
KDD
2008
ACM
115views Data Mining» more  KDD 2008»
16 years 3 days ago
Topical query decomposition
We introduce the problem of query decomposition, where we are given a query and a document retrieval system, and we want to produce a small set of queries whose union of resulting...
Francesco Bonchi, Carlos Castillo, Debora Donato, ...
ESA
2004
Springer
97views Algorithms» more  ESA 2004»
15 years 5 months ago
Radio Network Clustering from Scratch
Abstract. We propose a novel randomized algorithm for computing a dominating set based clustering in wireless ad-hoc and sensor networks. The algorithm works under a model which ca...
Fabian Kuhn, Thomas Moscibroda, Roger Wattenhofer
SIGIR
2010
ACM
14 years 12 months ago
Optimal meta search results clustering
By analogy with merging documents rankings, the outputs from multiple search results clustering algorithms can be combined into a single output. In this paper we study the feasibi...
Claudio Carpineto, Giovanni Romano
DASFAA
2003
IEEE
151views Database» more  DASFAA 2003»
15 years 5 months ago
Approximate String Matching in DNA Sequences
Approximate string matching on large DNA sequences data is very important in bioinformatics. Some studies have shown that suffix tree is an efficient data structure for approxim...
Lok-Lam Cheng, David Wai-Lok Cheung, Siu-Ming Yiu