| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2012: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
The purpose of this research project is to introduce validated numerics techniques to clustering, to solve the local convergence problem in clustering and to deal with the uncertainty of given data for clustering.
In local convergence problem, the all solution algorithm in validated numerics could not produce its result for entropy-regularized fuzzy c-means (eFCM) within adequate time because eFCM has many local optimal solutions. On the other hand, it was clarified that a maximizing model of Bezdek-type fuzzified c-means algorithm can be transformed into a trace maximizing problem naturally, which can be solved globally.
In dealing with the uncertainty of give data, an algorithm was constructed to represent given missing values as the interval with infinite width, and to execute clustering along with divide such the intervals with the aid of all solution algorithm in validated numerics.
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