A Hybrid Data Mining Approach for Knowledge Extraction and Classification in Medical Databases

This paper presents a novel hybrid data mining approach for knowledge extraction and classification in medical databases. The approach combines self organizing map, k-means and naive Bayes with a neural network based classifier. The idea is to cluster all data in soft clusters using neural and statistical clustering and fuse them using serial and parallel fusion in conjunction with a neural classifier. The approach has been implemented and tested on a benchmark medical database. The preliminary experiments are very promising.