Analyzing model distances between expert propositions with differently ordered logical variables of knowledge base formulas and collective clustering
This paper proves a theorem on the metrics taking into account the ordering (assigning of real numbers (0, 1) to variables of a formula) of elementary propositions in models by each expert and the degrees with which the models are scattered over the variables. This approach is proposed for the first...
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| Veröffentlicht in: | Pattern recognition and image analysis 2017, Vol.27 (1), p.29-35 |
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| container_title | Pattern recognition and image analysis |
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| creator | Vikent’ev, A. A. |
| description | This paper proves a theorem on the metrics taking into account the ordering (assigning of real numbers (0, 1) to variables of a formula) of elementary propositions in models by each expert and the degrees with which the models are scattered over the variables. This approach is proposed for the first time. Some examples demonstrating the novelty of the metrics are presented, and a method is proposed that allows a new metrics to be constructed based on previously obtained and/or already available metrics. |
| doi_str_mv | 10.1134/S1054661817010175 |
| format | Article |
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| identifier | ISSN: 1054-6618 |
| ispartof | Pattern recognition and image analysis, 2017, Vol.27 (1), p.29-35 |
| issn | 1054-6618 1555-6212 |
| language | eng |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Clustering Computer Science Consumer goods Formulas (mathematics) Image analysis Image Processing and Computer Vision Knowledge Knowledge base Mathematical Method in Pattern Recognition Mathematical models Order disorder Pattern Recognition Real numbers Studies Theorems |
| title | Analyzing model distances between expert propositions with differently ordered logical variables of knowledge base formulas and collective clustering |
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