METHODS FOR DETERMINING SIMILARITY OF CATEGORICAL ORDERED DATA
نویسندگان
چکیده
Context. The development of effective distance metrics and similarity measures for categorical features is an important task in data analysis, machine learning, decision theory since a significant portion object properties described by non-numerical values. Typically, the dependence between may be more complex than simply comparing them equality or inequality. Such attributes can relatively similar, to construct model, it necessary consider this when calculating measures.
 Objective. aim study improve efficiency solving practical analysis problems developing mathematical tools determining objects based on ordered features.
 Method. A weighted Manhattan measure ordinal (i.e. linear order with scales preference considering problem domain specified attribute value set) are proposed. It proven that formula satisfies axioms non-negativity, symmetry, triangle inequality, upper bound, therefore metric space ranked features. also presented boundedness, maximum minimum similarity, decreasing function.
 Results. developed approach has been implemented applied degree Conclusions. In study, were determine structured specific priority form ranking system preferences. Their analyzed. Experimental studies have shown convenience “intuitive understanding” logic processing problems. proposed provide opportunity conduct new meaningful research analysis. Prospects further lie experimental use tasks studying their effectiveness.
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ژورنال
عنوان ژورنال: Radio Electronics, Computer Science, Control
سال: 2023
ISSN: ['2313-688X', '1607-3274']
DOI: https://doi.org/10.15588/1607-3274-2023-2-4