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» Mining association rules from imprecise ordinal data
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PKDD
2005
Springer
110views Data Mining» more  PKDD 2005»
15 years 11 months ago
k-Anonymous Patterns
It is generally believed that data mining results do not violate the anonymity of the individuals recorded in the source database. In fact, data mining models and patterns, in orde...
Maurizio Atzori, Francesco Bonchi, Fosca Giannotti...
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, ...
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
16 years 6 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
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
PKDD
2009
Springer
134views Data Mining» more  PKDD 2009»
16 years 17 days ago
Mining Graph Evolution Rules
In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that describe the evolution of large networks over time, at a local level. Given a sequenc...
Michele Berlingerio, Francesco Bonchi, Björn ...