Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Data Engineering - Special issue on directions for future DBMS research and development
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Using Self-Similarity to Cluster Large Data Sets
Data Mining and Knowledge Discovery
Knowledge Mining by Imprecise Querying: A Classification-Based Approach
Proceedings of the Eighth International Conference on Data Engineering
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
IEEE Transactions on Knowledge and Data Engineering
On the development of a web-based system for transportation services
Information Sciences: an International Journal
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This paper reports on the exploitation of data mining techniques during the formulation of purposeful association rules out of the transactions' database of a transportation management system. The rules' construction is performed through an elaborated version of the AprioriTid algorithm. The proposed algorithm is generic and capable to construct such rules by creating a large set of related items. The constructed rules can be used by the system's recommender module, which is responsible for providing recommendations to the associated users. The recommendation process takes into account the constructed rules and techniques that derive from the area of Collaborative Filtering (CF).