Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Building a scalable and accurate copy detection mechanism
Proceedings of the first ACM international conference on Digital libraries
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
One-to-one marketing on the internet
ICIS '99 Proceedings of the 20th international conference on Information Systems
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Abstract-Driven Pattern Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Neural Networks and Customer Grouping in E-Commerce: A Framework Using Fuzzy ART
AIWORC '00 Proceedings of the Academia/Industry Working Conference on Research Challenges
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
A survey of data mining and knowledge discovery software tools
ACM SIGKDD Explorations Newsletter
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
OPUS: an efficient admissible algorithm for unordered search
Journal of Artificial Intelligence Research
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With the explosive growth of data in electronic commerce, rule finding becomes a crucial part in marketing. In this paper, we discuss the essential limitations of the existing metrics to quantify the interests of rules, and present the need of optimizing the interest metric. We describe the construction of the connection network that represents the relationships between items and propose a natural marketing model using the network. Although simple interest metrics were used, the connection network model showed stable performance in the experiment with field data. By constructing the network based on the optimized interest metric, the performance of the model was significantly improved.