Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of weighted association rules (WAR)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
A two-dimensional interpolation function for irregularly-spaced data
ACM '68 Proceedings of the 1968 23rd ACM national conference
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
Mining association rules in very large clustered domains
Information Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
Mining weighted association rules
Intelligent Data Analysis
Mining Weighted Association Rules without Preassigned Weights
IEEE Transactions on Knowledge and Data Engineering
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
Beyond distance measurement: constructing neighborhood similarity for video annotation
IEEE Transactions on Multimedia - Special section on communities and media computing
Information Sciences: an International Journal
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Incorporating pageview weight into an association-rule-based web recommendation system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Valency based weighted association rule mining
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Automatic Item Weight Generation for Pattern Mining and its Application
International Journal of Data Warehousing and Mining
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
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Association rule mining is an important data mining task that discovers relationships among items in a transaction database. Classical association rule mining approaches make the implicit assumption that an item's importance is determined by its support. In contrast, Weighted Association Rule Mining (WARM) attempts to provide a notion of importance, or weight to individual items that are not based solely on item support. Previous approaches to Weighted Association Rule Mining assign item weights in a subjective manner, based on a user's specialized knowledge of the underlying domain that is involved. Such approaches are infeasible when millions of items are present in a dataset, or when domain knowledge is unavailable. Furthermore, even when such domain information is available, a weight assignment based on subjective information constrains the knowledge discovered to fit with the weights assigned, thus inhibiting the discovery of new trends in the data. In this research we automate the process of weight assignment by formulating a linear model that captures relationships between items. This approach extends prior research based on the Valency model. We extend the Valency model by expanding the field of interaction beyond immediate neighborhoods and show that this leads to significant improvements in performance on a number of different metrics that we use.