A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Mining fuzzy association rules
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Effective Boolean Algorithm for Mining Association Rules in Large Databases
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
Exploiting data preparation to enhance mining and knowledgediscovery
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multi-level association rules for MP3P marketing strategies based on extensive marketing survey data
Expert Systems with Applications: An International Journal
A collaborative filtering approach to mitigate the new user cold start problem
Knowledge-Based Systems
Knowledge-Based Systems
Hi-index | 12.06 |
A novel, practical and efficient strategy for providing personalization for online shoppers is proposed. Based on shoppers' previous purchasing behavior and the customer's previous choices the strategy is capable of suggesting relevant and desirable products to each customer accurately. The strategy is based on training a back-propagation neural network with association rules that are mined from a transactional database. Unlike most strategies that only consider the relationship between the purchased items, the proposed strategy also incorporates additional influential attributes such as the price of items and their merchandise category. A powerful confidence estimate is used to rank the suggestions. Experimental evidence is provided to demonstrate the effectiveness of the proposed strategy.