Метрические свойства разбиений множеств произвольной природы
The interpretation of data content is closely connected with partition analysis. Different applications require different detailings of data partitions. For a system to be successful in a variety of problems, several partitions have to be ensured for cognitive-like techniques. A rational combination...
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| Date: | 2007 |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | Russian |
| Published: |
Видавничий дім "Академперіодика" НАН України
2007
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| Subjects: | |
| Online Access: | http://dspace.nbuv.gov.ua/handle/123456789/1815 |
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| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | Метрические свойства разбиений множеств произвольной природы / А.Г. Каграманян, В.П. Машталир, Е.В. Скляр, В.В. Шляхов // Доп. НАН України. — 2007. — N 6. — С. 35–39. — Бібліогр.: 6 назв. — рос. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| Summary: | The interpretation of data content is closely connected with partition analysis. Different applications require different detailings of data partitions. For a system to be successful in a variety of problems, several partitions have to be ensured for cognitive-like techniques. A rational combination of low-level and high-level capabilities seems to be the most promising way to significantly improve the data understanding integrally. To reduce the gap between low-level features and high-level semantics in clustering, we propose, ground, and explore a new metric on partitions of an arbitrary measurable set. |
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