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» Approximate clustering via core-sets
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KDD
2009
ACM
188views Data Mining» more  KDD 2009»
14 years 8 months ago
Mining discrete patterns via binary matrix factorization
Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rankone binary matrix approximations that identify the dominant patt...
Bao-Hong Shen, Shuiwang Ji, Jieping Ye
KDD
2012
ACM
196views Data Mining» more  KDD 2012»
11 years 10 months ago
Chromatic correlation clustering
We study a novel clustering problem in which the pairwise relations between objects are categorical. This problem can be viewed as clustering the vertices of a graph whose edges a...
Francesco Bonchi, Aristides Gionis, Francesco Gull...
DATAMINE
2006
142views more  DATAMINE 2006»
13 years 7 months ago
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized infor...
Hye-Chung Kum, Joong Hyuk Chang, Wei Wang 0010
PODS
2009
ACM
119views Database» more  PODS 2009»
14 years 8 months ago
Exceeding expectations and clustering uncertain data
Database technology is playing an increasingly important role in understanding and solving large-scale and complex scientific and societal problems and phenomena, for instance, un...
Sudipto Guha, Kamesh Munagala
IAT
2005
IEEE
14 years 1 months ago
Decomposing Large-Scale POMDP Via Belief State Analysis
Partially observable Markov decision process (POMDP) is commonly used to model a stochastic environment with unobservable states for supporting optimal decision making. Computing ...
Xin Li, William K. Cheung, Jiming Liu