| Autor: |
Tianxing, Man, Zhukova, Nataly, Vodyaho, Alexander, Myo Thaw, Aung, Mustafin, Nikolay |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
|
| Zdroj: |
Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 26, Iss 2, Pp 667-674 (2020) |
| DOI: |
10.5281/zenodo.4007457 |
| Popis: |
In real life, extracting from real data through data mining is a complicated process. Meta-learning helps optimize algorithm parameters to improve the performance of data mining. And semantic meta mining helps build workflows based on knowledge models. This paper proposes a data mining ontology integration framework for adaptive data processing based on the concept of semantic meta mining. It allows building domain-oriented ontology for data mining tasks. The ontology helps to choose suitable solutions and format the processing process based on data characteristics and task requirements. For helping to process the data sets adaptively, an ontology merging method is presented for the application of the proposed ontology in various domains. As an example, this article presents the use of the proposed ontology and method on the domain of time series classification. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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