Clustering Based Classification in Data Mining Method Recommendation

With the growing amount of data available in today´s world, the emphasis is laid on the automatic configuration of data analysis - metal earning. This paper elaborates one of the metal earning sub problems, 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.