| Budget Amount *help |
¥47,060,000 (Direct Cost: ¥36,200,000、Indirect Cost: ¥10,860,000)
Fiscal Year 2015: ¥9,490,000 (Direct Cost: ¥7,300,000、Indirect Cost: ¥2,190,000)
Fiscal Year 2014: ¥9,490,000 (Direct Cost: ¥7,300,000、Indirect Cost: ¥2,190,000)
Fiscal Year 2013: ¥9,490,000 (Direct Cost: ¥7,300,000、Indirect Cost: ¥2,190,000)
Fiscal Year 2012: ¥9,490,000 (Direct Cost: ¥7,300,000、Indirect Cost: ¥2,190,000)
Fiscal Year 2011: ¥9,100,000 (Direct Cost: ¥7,000,000、Indirect Cost: ¥2,100,000)
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| Outline of Final Research Achievements |
Data mining technologies for extracting valuable information from big data are significantly important nowadays. The main purpose of conventional data mining technologies is to extract superficial statistical patterns from data. We are rather concerned with the issue of how to learn latent information behind data and how to detect its changes, which we call “latent dynamics.” We construct a theory for learning latent dynamics from a unifying view of information-theoretic learning theory, specifically on the basis of the minimum description length principle. We also demonstrate its effectiveness through the applications to real world data (e.g. security, SNS, marketing, healthcare, education, etc.).
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