Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A Recommendation Algorithm Using Multi-Level Association Rules
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Generating concise association rules
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Deriving non-redundant approximate association rules from hierarchical datasets
Proceedings of the 17th ACM conference on Information and knowledge management
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Association rule mining and recommender systems are two popular methods for obtaining knowledge and information from datasets. However, both of these methods suffer from limitations. Traditionally association rule mining has focused on extracting as many rules as possible from flat datasets. More recently, issues over the number of rules and obtaining rules from datasets with multiple concept levels have come into focus. Recommender systems have been popular with users when it comes to helping find similar interests to those they already have. However, recommender systems suffer from two major problems, cold start and novelty. The aims of our research is to develop an approach for extracting non-redundant multi-level and cross-level association rules from datasets with multiple concept levels and utilise them in a recommender system with the aim of potentially solving the cold start and novelty problems.