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112
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BMCBI
2010
118views more  BMCBI 2010»
15 years 22 days ago
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification
Background: Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limit...
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Pa...
98
Voted
PAKDD
2009
ACM
170views Data Mining» more  PAKDD 2009»
15 years 9 months ago
Discovering Periodic-Frequent Patterns in Transactional Databases.
Since mining frequent patterns from transactional databases involves an exponential mining space and generates a huge number of patterns, efficient discovery of user-interest-based...
Byeong-Soo Jeong, Chowdhury Farhan Ahmed, Syed Kha...
82
Voted
KDD
1998
ACM
120views Data Mining» more  KDD 1998»
15 years 4 months ago
Large Datasets Lead to Overly Complex Models: An Explanation and a Solution
This paper explores unexpected results that lie at the intersection of two common themes in the KDD community: large datasets and the goal of building compact models. Experiments ...
Tim Oates, David Jensen
CIS
2007
Springer
15 years 6 months ago
Mining with Noise Knowledge: Error Aware Data Mining
—Real-world data mining deals with noisy information sources where data collection inaccuracy, device limitations, data transmission and discretization errors, or man-made pertur...
Xindong Wu
EDBT
2000
ACM
15 years 4 months ago
Mining Classification Rules from Datasets with Large Number of Many-Valued Attributes
Decision tree induction algorithms scale well to large datasets for their univariate and divide-and-conquer approach. However, they may fail in discovering effective knowledge when...
Giovanni Giuffrida, Wesley W. Chu, Dominique M. Ha...