Name
Abstract | ||
|---|---|---|
Data mining in the form of rule discovery is a growing field of investigation. A recent addition to this field is the use of evolutionary algorithms in the mining process. While this has been used extensively in the traditional mining of relational databases, it has hardly, if at all, been used in mining sequences and time series. In this paper we describe our method for evolutionary sequence mining, using a specialized piece of hardware for rule evaluation, and show how the method can be applied to several different mining tasks, such as supervised sequence prediction, unsupervised mining of interesting rules, discovering connections between separate time series, and investigating tradeoffs between contradictory objectives by using multiobjective evolution. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/s10994-005-5823-8 | Machine Learning |
Keywords | Field | DocType |
sequence mining,knowledge discovery,time series,genetic programming,specialized hardware | Data mining,Data stream mining,Concept mining,Evolutionary algorithm,Molecule mining,Genetic programming,Unsupervised learning,Association rule learning,Knowledge extraction,Artificial intelligence,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
58 | 2-3 | 0885-6125 |
Citations | PageRank | References |
9 | 0.54 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Magnus Lie Hetland | 1 | 73 | 8.04 |
| Pål Sætrom | 2 | 85 | 7.84 |
| HetlandMagnus Lie | 3 | 9 | 0.54 |