Abstrakt
With the growing amount of data available in today’s world, the emphasis is laid on the automatic configuration of data analysis – metalearning. This paper elaborates one of the metalearning subproblems, the data mining method recommendation.
Based on a metric over the data features called metadata, we have proposed a solution exploiting clustering of datasets. The agglomerative algorithm is used to construct clustering over the metadata, and the average methods’ performance is computed in each cluster.
The ranking of data mining methods is then deduced from the classification of a dataset to a particular cluster. The recommendation algorithm, which is implemented within our data mining multi-agent system, has been tested in various configurations, and the results of these experiments have been compared.