| Budget Amount *help |
¥21,580,000 (Direct Cost: ¥16,600,000、Indirect Cost: ¥4,980,000)
Fiscal Year 2020: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2019: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Fiscal Year 2017: ¥9,230,000 (Direct Cost: ¥7,100,000、Indirect Cost: ¥2,130,000)
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| Outline of Final Research Achievements |
Because of development of a variety of sensor devices and increase of the capacity of storage devices, a significance of data-driven approaches to extracting useful knowledge from accumulated data have been widely recognized. In this study, we consider identifying important substructures in sequence or more complicated graph data, which has a connected structure inside a data instance. Since combinatorially many possible substructures exist, we propose efficient pruning algorithms that removes unnecessary patterns. Our framework is based on an optimization theory, and thus, the optimality of the resulting identified substructures can be guaranteed.
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