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EDBT
2011
ACM
199views Database» more  EDBT 2011»
14 years 6 months ago
Finding closed frequent item sets by intersecting transactions
Most known frequent item set mining algorithms work by enumerating candidate item sets and pruning infrequent candidates. An alternative method, which works by intersecting transa...
Christian Borgelt, Xiaoyuan Yang, Rubén Nog...
TSP
2008
151views more  TSP 2008»
15 years 2 months ago
Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
The rapid developing area of compressed sensing suggests that a sparse vector lying in a high dimensional space can be accurately and efficiently recovered from only a small set of...
Moshe Mishali, Yonina C. Eldar
KDD
2002
ACM
140views Data Mining» more  KDD 2002»
16 years 3 months ago
Mining frequent item sets by opportunistic projection
In this paper, we present a novel algorithm OpportuneProject for mining complete set of frequent item sets by projecting databases to grow a frequent item set tree. Our algorithm ...
Junqiang Liu, Yunhe Pan, Ke Wang, Jiawei Han
SSDBM
2003
IEEE
83views Database» more  SSDBM 2003»
15 years 8 months ago
PiQA: An Algebra for Querying Protein Data Sets
Life science researchers frequently need to query large protein data sets in a variety of different ways. Protein data sets have a rich structure that includes its primary structu...
Sandeep Tata, Jignesh M. Patel
CORR
2006
Springer
153views Education» more  CORR 2006»
15 years 3 months ago
Genetic Programming, Validation Sets, and Parsimony Pressure
Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited n...
Christian Gagné, Marc Schoenauer, Marc Pari...