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Data Mining and Knowledge Discovery
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We present an adaptation of model-based clustering for partially labeled data, that is capable of finding hidden cluster labels. All the originally known and discoverable clusters are represented using localized feature subset selections (subspaces), obtaining clusters unable to be discovered by global feature subset selection. The semi-supervised projected model-based clustering algorithm (SeSProC) also includes a novel model selection approach, using a greedy forward search to estimate the final number of clusters. The quality of SeSProC is assessed using synthetic data, demonstrating its effectiveness, under different data conditions, not only at classifying instances with known labels, but also at discovering completely hidden clusters in different subspaces. Besides, SeSProC also outperforms three related baseline algorithms in most scenarios using synthetic and real data sets.