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» Mining Large Itemsets for Association Rules
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KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 7 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 ...
HICSS
2005
IEEE
164views Biometrics» more  HICSS 2005»
14 years 1 months ago
An Efficient Technique for Frequent Pattern Mining in Real-Time Business Applications
Association rule mining in real-time is of increasing thrust in many business applications. Applications such as e-commerce, recommender systems, supply-chain management and group...
Rajanish Dass, Ambuj Mahanti
HICSS
2003
IEEE
171views Biometrics» more  HICSS 2003»
14 years 21 days ago
Improving the Efficiency of Interactive Sequential Pattern Mining by Incremental Pattern Discovery
The discovery of sequential patterns, which extends beyond frequent item-set finding of association rule mining, has become a challenging task due to its complexity. Essentially, ...
Ming-Yen Lin, Suh-Yin Lee
KDD
2002
ACM
196views Data Mining» more  KDD 2002»
14 years 7 months ago
Comparing Two Recommender Algorithms with the Help of Recommendations by Peers
Abstract. Since more and more Web sites, especially sites of retailers, offer automatic recommendation services using Web usage mining, evaluation of recommender algorithms has bec...
Andreas Geyer-Schulz, Michael Hahsler
CLIMA
2004
13 years 8 months ago
The Apriori Stochastic Dependency Detection (ASDD) Algorithm for Learning Stochastic Logic Rules
Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environm...
Christopher Child, Kostas Stathis