Abstract
The high cost of labeling data for analysis increased the interest in semi-supervised learning, especially constrained clustering, which usually involves reduced cost. At the same time, Active Learning (AL) aims to minimize the cost of creating labeled datasets by trying to identify which unlabeled data are more relevant for using during the learning process, considering which labels are already available. This paper proposes four AL strategies to an evolutionary constrained clustering algorithm (FIECE-EM) based on Gaussian Mixture Models (GMM), with corresponding theoretical asymptotic analyses. These strategies use information from many different sources, such as the partition, the population, and even specific aspects of the algorithm. We perform empirical evaluation on 14 well-known datasets, as a way to measure the impacts of each strategy both in relation to accuracy and labeling cost. The results were compared with baseline supervised algorithms as well as COBRAS, a state-of-the-art Active Learning algorithm for constrained clustering. Three of the proposed strategies obtained significantly better results than COBRAS in our empirical evaluation. Thus, the combination of FIECE-EM with these strategies can be considered viable alternatives for AL in a constrained clustering setting.
Published Version
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