Classification of large scale qualitative data and visualization of their structure.

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To perform knowledge discovery from large data sets as signals from various sensors or texts accumulated in the web, it is required to classify into groups which have some common attribute as a pretreatment for detailed analysis even for such large and irregular data sets. Though such data sets contain a large number of items, it is often sufficient to use a small number of items for reasonable classification. We have studied mainly variable selection and regularization method for classification of qualitative data both from theoretical analysis and numerical experiments. These results have applied to the system for estimating human behavior using the data from the infrared sensors placed in the ceiling of the room.