Data mining standards initiatives
Communications of the ACM - Evolving data mining into solutions for insights
Advances in Distributed and Parallel Knowledge Discovery
Advances in Distributed and Parallel Knowledge Discovery
Combining email models for false positive reduction
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Understanding the network-level behavior of spammers
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Personalized Spam Filtering with Semi-supervised Classifier Ensemble
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Exploiting machine learning to subvert your spam filter
LEET'08 Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats
Partitioned logistic regression for spam filtering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Spamalytics: an empirical analysis of spam marketing conversion
Proceedings of the 15th ACM conference on Computer and communications security
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Data strip mining for the virtual design of pharmaceuticals with neural networks
IEEE Transactions on Neural Networks
Spam email filtering using network-level properties
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Data mining with neural networks and support vector machines using the R/rminer tool
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Context-aware collaborative data stream mining in ubiquitous devices
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Classification of textual E-mail spam using data mining techniques
Applied Computational Intelligence and Soft Computing
| Hi-index | 0.01 |
Unsolicited e-mail (spam) is a severe problem due to intrusion of privacy, online fraud, viruses and time spent reading unwanted messages. To solve this issue, Collaborative Filtering (CF) and Content-Based Filtering (CBF) solutions have been adopted. We propose a new CBF-CF hybrid approach called Symbiotic Data Mining (SDM), which aims at aggregating distinct local filters in order to improve filtering at a personalized level using collaboration while preserving privacy. We apply SDM to spam e-mail detection and compare it with a local CBF filter (i.e. Naive Bayes). Several experiments were conducted by using a novel corpus based on the well known Enron datasets mixed with recent spam. The results show that the symbiotic strategy is competitive in performance when compared to CBF and also more robust to contamination attacks.