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KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 8 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 ...
SPAA
1997
ACM
13 years 11 months ago
A Localized Algorithm for Parallel Association Mining
Discovery of association rules is an important database mining problem. Mining for association rules involves extracting patterns from large databases and inferring useful rules f...
Mohammed Javeed Zaki, Srinivasan Parthasarathy, We...
ICTAI
2003
IEEE
14 years 23 days ago
Parallel Mining of Maximal Frequent Itemsets from Databases
In this paper, we propose a parallel algorithm for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. The new par...
Soon Myoung Chung, Congnan Luo
AAI
2007
132views more  AAI 2007»
13 years 7 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...
AI
2004
Springer
14 years 27 days ago
Distributed Data Mining vs. Sampling Techniques: A Comparison
To address the of mining a huge volume of geographically distributed databases, we propose two approaches. The first one is to download only a sample of each database. The second ...
Mohamed Aounallah, Sébastien Quirion, Guy W...