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» Evaluation of Sampling for Data Mining of Association Rules
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KDD
2002
ACM
138views Data Mining» more  KDD 2002»
16 years 1 months ago
Learning to match and cluster large high-dimensional data sets for data integration
Part of the process of data integration is determining which sets of identifiers refer to the same real-world entities. In integrating databases found on the Web or obtained by us...
William W. Cohen, Jacob Richman
111
Voted
CORR
2010
Springer
279views Education» more  CORR 2010»
15 years 24 days ago
Mining Frequent Itemsets Using Genetic Algorithm
In general frequent itemsets are generated from large data sets by applying association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental, Border algorithm...
Soumadip Ghosh, Sushanta Biswas, Debasree Sarkar, ...
126
Voted
ICDM
2005
IEEE
139views Data Mining» more  ICDM 2005»
15 years 6 months ago
Approximate Inverse Frequent Itemset Mining: Privacy, Complexity, and Approximation
In order to generate synthetic basket data sets for better benchmark testing, it is important to integrate characteristics from real-life databases into the synthetic basket data ...
Yongge Wang, Xintao Wu
209
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ICDE
2008
IEEE
153views Database» more  ICDE 2008»
16 years 2 months ago
Mining Views: Database Views for Data Mining
We present a system towards the integration of data mining into relational databases. To this end, a relational database model is proposed, based on the so called virtual mining vi...
Élisa Fromont, Adriana Prado, Bart Goethals...
134
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ADMA
2010
Springer
248views Data Mining» more  ADMA 2010»
14 years 10 months ago
Classification Inductive Rule Learning with Negated Features
This paper reports on an investigation to compare a number of strategies to include negated features within the process of Inductive Rule Learning (IRL). The emphasis is on generat...
Stephanie Chua, Frans Coenen, Grant Malcolm