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» Mining association rules from imprecise ordinal data
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153
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KDD
1998
ACM
122views Data Mining» more  KDD 1998»
15 years 10 months ago
Memory Placement Techniques for Parallel Association Mining
Many data mining tasks (e.g., Association Rules, Sequential Patterns) use complex pointer-based data structures (e.g., hash trees) that typically suffer from sub-optimal data loca...
Srinivasan Parthasarathy, Mohammed Javeed Zaki, We...
BMCBI
2007
167views more  BMCBI 2007»
15 years 6 months ago
Applying negative rule mining to improve genome annotation
Background: Unsupervised annotation of proteins by software pipelines suffers from very high error rates. Spurious functional assignments are usually caused by unwarranted homolog...
Irena I. Artamonova, Goar Frishman, Dmitrij Frishm...
145
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ECAI
2008
Springer
15 years 8 months ago
Extracting Semantic Annotations from Moodle Data
The purpose of this paper is to provide a solution which allows automatic reasoning processes over Moodle activities logs, in order to obtain user-personalized recommendations. Act...
Mihai Gabroveanu, Ion-Mircea Diaconescu
AAI
2007
132views more  AAI 2007»
15 years 6 months ago
Incremental Extraction of Association Rules in Applicative Domains
In recent years, the KDD process has been advocated to be an iterative and interactive process. It is seldom the case that a user is able to answer immediately with a single query...
Arianna Gallo, Roberto Esposito, Rosa Meo, Marco B...
PKDD
2004
Springer
141views Data Mining» more  PKDD 2004»
15 years 11 months ago
Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach
In this paper we propose a novel spatial associative classifier method based on a multi-relational approach that takes spatial relations into account. Classification is driven by s...
Michelangelo Ceci, Annalisa Appice, Donato Malerba