Unsupervised topographic learning for spatiotemporal data mining
Advances in Artificial Intelligence - Special issue on machine learning paradigms for modeling spatial and temporal information in multimedia data mining
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In data mining, the problem of measuring similarities between different subsets is an important issue which has been little investigated up to now. In this paper, a novel method is proposed based on unsupervised learning. Different subsets of a dataset are characterized by means of a model which implicitly corresponds to a set of prototypes, each one capturing a different modality of the data. Then, structural differences between two subsets are reflected in the corresponding model. Differences between models are detected using a similarity measure based on data density. Experiments over synthetic and real datasets illustrate the effectiveness, efficiency, and insights provided by our approach.