Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
New algorithms for efficient mining of association rules
Information Sciences: an International Journal
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Web page clustering using a self-organizing map of user navigation patterns
Decision Support Systems - Special issue: Web data mining
Discovering user profiles for web personalized recommendation
Journal of Computer Science and Technology
Intelligent web traffic mining and analysis
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Feature-based recommendations for one-to-one marketing
Expert Systems with Applications: An International Journal
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
CBAR: an efficient method for mining association rules
Knowledge-Based Systems
Self organization of a massive document collection
IEEE Transactions on Neural Networks
A Methodology for Exploring Association Models
Visual Data Mining
Multi-level association rules for MP3P marketing strategies based on extensive marketing survey data
Expert Systems with Applications: An International Journal
A hybrid of sequential rules and collaborative filtering for product recommendation
Information Sciences: an International Journal
Using the Taguchi method for effective market segmentation
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper proposes an anticipation model of potential customers' purchasing behavior. This model is inferred from past purchasing behavior of loyal customers and the web server log files of loyal and potential customers by means of clustering analysis and association rules analysis. Clustering analysis collects key characteristics of loyal customers' personal information; these are used to locate other potential customers. Association rules analysis extracts knowledge of loyal customers' purchasing behavior, which is used to detect potential customers' near-future interest in a star product. Despite using offline analysis to filter out potential customers based on loyal customers' personal information and generate rules of loyal customers' click streams based on loyal customers' web log data, an online analysis which observes potential customers' web logs and compares it with loyal customers' click stream rules can more readily target potential customers who may be interested in the star products in the near future.