Knowledge discovery from high-dimensional data based on combinatorial computation
Finished by March 31, 2014
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![Yoshinobu Kawahara](../../researcher/3ki/image/kawahara.gif)
Osaka University, The Institute of Scientific and Industrial Research, Associate Professor
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Against the backdrop of accelerating progress of data acquisition technologies, there are more scenes where we deal with high-dimensional data in a variety of engineering problems, such as bioinformatics, natural language processing and image data processing. The purpose of this research is to build a data-mining framework for global analysis of high-dimensional data based on combinatorial computation, using the discrete data structure called submodularity. And, we aim at discovering important knowledge in a variety of applications by applying the developed algorithms to real-world data.
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