| 研究実績の概要 |
Grouping is a common technique used for organizing and understanding entities. Yet, historical aspects are often quite important as, in many cases, the history shapes and defines the present characteristics of entities. We have proposed methods for grouping entities based on their histories. To enable history-based entity grouping, we formulate the latent history-based category hypothesis, which states that entities can be categorized based on the similarity of their histories, such that entities included in the same category have more similar histories to each other than to ones in other categories. In order to group entities based on their histories, we propose a concise optimization model inspired by the popular Affinity Propagation (AP) algorithm for exemplar-based clustering.
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