Development of probabilistic transition models using self-organizing of a large-scale histrical data on space-time dimension

在时空维度上分析大规模的历史资料时，历史的多样性是一个难点。在这项研究中，我们提出的方法来开发历史中的过渡的概率模型，使用它们的拓扑结构和结构的子序列的历史。提出了柔性自组织映射（FSOM），它是一种根据历史的拓扑结构自动调整映射范围的自组织映射。通过从FSOM等导出的结构把握历史中的变迁，我们用商业或金融业务中的服务使用历史、收据等医疗服务历史、城市空间中的移动历史等真实的世界数据验证了我们的方法。

When we analyze a large-scale historical data on space-time dimension, there is the difficulty with the variety of the history. In this study, we proposed methodologies to develop probabilistic models of the transitions in histories, using their topological structure and the structure of the subsequence of history. We develop flexible self-organization map (FSOM), which is a SOM whose map range is automatically adjusted by the topological structure of history. Transitions in histories are grasped by the structure derived from the FSOM and so on. We verified our methodologies with the real world data, for example, service use history in merchandising or the finance business, health care service histories such as the receipt, and movement histories in city space.