Séminaire Donnees et APprentissage Artificiel
Clustering-based Models from Model-based Clustering
Speaker(s) : Mika Sato-Ilic (University of Tsukuba)
Recent advances in the area of information science have enabled the collection of multi-source data and complex data in vast amounts. Data analysis has been tasked with the increasingly significant mission of dealing with such data. Clustering is one type of data analysis used to detect and characterize the latent structure of data by classifying the objects based on similarities among objects. Model-based clustering is a framework of clustering methods and main issue of this is an assumption of a model to the data and by fitting the model to data, an adjusted partition will be estimated. Although this approach has the benefit of obtaining a clear solution as the result of the partition based on mathematical theory, we cannot avoid the risk the previously assumed model might not adjust to the latent classification structure of the data. Therefore, we propose a framework called clustering-based models in which we exploit obtained clustering result as a scale of latent structure of the data and apply it to the observed data, and then apply the modified data to a model in order to obtain a more accurate result. In this talk, several methods in this framework called clustering-based models with several applications will be introduced.
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