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HPCC
2007
Springer
14 years 3 months ago
A Data Imputation Model in Sensor Databases
Data missing is a common problem in database query processing, which can cause bias or lead to inefficient analyses, and this problem happens more often in sensor databases. The re...
Nan Jiang
ICDM
2008
IEEE
130views Data Mining» more  ICDM 2008»
14 years 3 months ago
Mining Temporal Patterns with Quantitative Intervals
In this paper we consider the problem of discovering frequent temporal patterns in a database of temporal sequences, where a temporal sequence is a set of items with associated da...
Thomas Guyet, Rene Quiniou
DMKD
2003
ACM
96views Data Mining» more  DMKD 2003»
14 years 2 months ago
Using transposition for pattern discovery from microarray data
We analyze expression matrices to identify a priori interesting sets of genes, e.g., genes that are frequently co-regulated. Such matrices provide expression values for given biol...
François Rioult, Jean-François Bouli...
DKE
2002
218views more  DKE 2002»
13 years 8 months ago
Computing iceberg concept lattices with T
We introduce the notion of iceberg concept lattices and show their use in knowledge discovery in databases. Iceberg lattices are a conceptual clustering method, which is well suit...
Gerd Stumme, Rafik Taouil, Yves Bastide, Nicolas P...
COMPUTE
2010
ACM
14 years 1 months ago
Mining periodic-frequent patterns with maximum items' support constraints
The single minimum support (minsup) based frequent pattern mining approaches like Apriori and FP-growth suffer from“rare item problem”while extracting frequent patterns. That...
R. Uday Kiran, P. Krishna Reddy