Name
Abstract | ||
|---|---|---|
The promise of Hybrid Systems as the next generation of Data Mining systems is investigated. This work is motivated by the obvious limitations of paradigms for Data Mining that are being used in isolation. We present a classification of hybrid systems based on the level of interaction between the component paradigms and present example objectives for the development of such systems. We highlight possible hybrid solutions by discussing various hybrid systems that may be developed to enhance the Nearest Neighbour algorithm. These include statistical measures to enhance distance measures, a loose coupling with Neural Networks or alternatively a tight coupling with genetic algorithms, to discover attribute weights. We also establish enhancements to the k-NN that make it appropriate for use as a paradigm for addressing regression data mining goals. We provide results obtained using these systems, comparing them with more traditional paradigms used to solve regression goals within Data Mining. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/3-540-64383-4_2 | PAKDD |
Keywords | Field | DocType |
next generation,hybrid data mining systems,genetic algorithm,hybrid system,tight coupling,neural network,data mining | Nearest neighbour algorithm,Information system,Data mining,Computer science,Loose coupling,Artificial intelligence,Artificial neural network,Hybrid system,Machine learning,Genetic algorithm,Knowledge acquisition,Distance measures | Conference |
Volume | ISSN | ISBN |
1394 | 0302-9743 | 3-540-64383-4 |
Citations | PageRank | References |
5 | 0.60 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarabjot S. Anand | 1 | 305 | 46.46 |
| John G. Hughes | 2 | 326 | 59.84 |