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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 6 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
166
Voted
CORR
2010
Springer
279views Education» more  CORR 2010»
15 years 6 months 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, ...
ICDM
2005
IEEE
139views Data Mining» more  ICDM 2005»
15 years 11 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
289
Voted
ICDE
2008
IEEE
153views Database» more  ICDE 2008»
16 years 7 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...
ADMA
2010
Springer
248views Data Mining» more  ADMA 2010»
15 years 4 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