On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Database Mining: A Performance Perspective
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
Node similarity in the citation graph
Knowledge and Information Systems
Modeling a Store's Product Space as a Social Network
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Mining of Massive Datasets
A product network analysis for extending the market basket analysis
Expert Systems with Applications: An International Journal
Dark Web portal overlapping community detection based on topic models
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
Overlapping community detection in networks: The state-of-the-art and comparative study
ACM Computing Surveys (CSUR)
| Hi-index | 12.05 |
A common problem for many companies, like retail stores, it is to find sets of products that are sold together. The only source of information available is the history of sales transactional data. Common techniques of market basket analysis fail when processing huge amounts of scattered data, finding meaningless relationships. We developed a novel approach for market basket analysis based on graph mining techniques, able to process millions of scattered transactions. We demonstrate the effectiveness of our approach in a wholesale supermarket chain and a retail supermarket chain, processing around 238,000,000 and 128,000,000 transactions respectively compared to classical approach.