Abstract
The article touches upon one of the main problems of machine learning - clustering objects. It has been widely used in various subject areas: marketing, sociology, psychology, etc. Clusterization algorithms, as a rule, are based on a metric that reflects the distance between objects. However, in some cases it is not practical to use the distance between objects. In certain situations, it is possible to say that one object is similar to the other, the latter being not similar to the former. The original picture and its copy may serve as an example. For such cases, a measure of object similarity is proposed in the work, which shows how many features of one object are contained in another one. A similarity matrix is built on this measure, the analysis of which allows revealing clusters of mutually similar objects. When testing the proposed clustering method, the Rand index (the proportion of correctly connected or unrelated objects) made 0.93. There has been proposed an algorithm that allows to form a set of objects absolutely different from each other. A set of objects formed in this way can later become a learning set for classifiers and increase their fidelity in recognition.
Highlights
The article touches upon one of the main problems of machine learning - clustering objects. It has been widely used in various subject areas: marketing, sociology, psychology, etc
Clusterization algorithms, as a rule, are based on a metric that reflects the distance between objects
It is possible to say that one object is similar to the other, the latter being not similar to the former
Summary
При решении которых возникают некоторые нюансы, например, если мы рассматриваем задачу определения степени схожести объектов в некоторых предметных областях. В данном случае также получается, что «расстояние» от будки до дома отличается от «расстояния» от дома до будки. Рассмотрим свойства введенной меры схожести: − если ни одно из значений признака объекта A не входит в множество значений признака объекта B, то ρ(A, B) = 0; − если все значения признаков объекта A входят в объект B, то ρ(A, B) = 1; − в общем случае ρ(A, B) ≠ ρ(B, A); − объекты A и B являются взаимно схожими в равной степени, если ρ(A, B) ≈ ρ(B, A) ≈ 1. Для любого объекта A из обучающего множества можно рассчитать меру схожести ρ(A, A), которая при этом всегда будет равна 1; − антисимметричность: ∀x, y ∈M(xRy&Rx ⇒ x = y). Стена Крыша Окно Дверь Труба Балкон Веранда Ступеньки Гараж ρ (Д1, Д2) 5/9 ≈ 0,56
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
