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» Mining frequent itemsets in time-varying data streams
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KES
2004
Springer
15 years 3 months ago
FIT: A Fast Algorithm for Discovering Frequent Itemsets in Large Databases
Association rule mining is an important data mining problem that has been studied extensively. In this paper, a simple but Fast algorithm for Intersecting attribute lists using a ...
Jun Luo, Sanguthevar Rajasekaran
ICMCS
2006
IEEE
344views Multimedia» more  ICMCS 2006»
15 years 3 months ago
Pattern Mining in Visual Concept Streams
Pattern mining algorithms are often much easier applied than quantitatively assessed. In this paper we address the pattern evaluation problem by looking at both the capability of ...
Lexing Xie, Shih-Fu Chang
DKE
2008
124views more  DKE 2008»
14 years 9 months ago
A MaxMin approach for hiding frequent itemsets
In this paper, we are proposing a new algorithmic approach for sanitizing raw data from sensitive knowledge in the context of mining of association rules. The new approach (a) rel...
George V. Moustakides, Vassilios S. Verykios
DIS
2009
Springer
15 years 4 months ago
A Sliding Window Algorithm for Relational Frequent Patterns Mining from Data Streams
Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered...
Fabio Fumarola, Anna Ciampi, Annalisa Appice, Dona...
ICDE
2001
IEEE
163views Database» more  ICDE 2001»
15 years 11 months ago
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
We present a new algorithm for mining maximal frequent itemsets from a transactional database. Our algorithm is especially efficient when the itemsets in the database are very lon...
Douglas Burdick, Manuel Calimlim, Johannes Gehrke