Unsupervised Meta-Learning for Clustering Algorithm Recommendation

In this work, the goal is to use clustering algorithms as recommender in a meta-learning system and, thus, to propose an unsupervised meta-learning approach. Meta-learning has been successfully used for recommendation of Machine Learning algorithms in several Data Mining tasks. Meta-learning can rank algorithms according to their adequacy for a new dataset and use this ranking to recommend algorithms. The recommendations are usually made by predictive meta-models induced by supervised Machine Learning techniques, therefore needing a target attribute. In many situations, the target attribute is not available or has a high computational cost. In these situations, the use of unsupervised meta-models (as clustering algorithms) could provide important insights from Machine Learning experiments, like the interpretation of the partitions found by these clustering algorithms. Here, clustering algorithms are used as unsupervised meta-models. Experimental results show that the proposed approach achieved better clustering quality.

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