New approach to clustering random attributes

• Autorzy: Gniazdowski Zenon

Identyfikatory

DOI

10.26348/znwwsi.31.41

• Języki publikacji: EN

Abstrakty

EN

This paper proposes a new method for similarity analysis and, conse- quently, a new algorithm for clustering different types of random attributes, both numerical and nominal. However, in order for nominal attributes to be clustered, their values must be properly encoded. In the encoding pro- cess, nominal attributes obtain a new representation in numerical form. Only the numeric attributes can be subjected to factor analysis, which allows them to be clustered in terms of their similarity to factors. The proposed method was tested for several sample datasets. It was found that the proposed method is universal. On the one hand, the method allows clustering of numerical attributes. On the other hand, it provides the ability to cluster nominal attributes. It also allows simultaneous clustering of numerical attributes and numerically encoded nominal attributes.

Słowa kluczowe

EN

numerical attributes; nominal attributes; numerical encoding; similarity of random attributes; clustering of random attributes; factor analysis

• Wydawca: Warszawska Wyższa Szkoła Informatyki
• Czasopismo: Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
• Rocznik: 2024
• Tom: nr 31
• Strony: 41--90
• Opis fizyczny: Bibliogr. 24 poz., rys., tab., wykr.

Twórcy

autor

Gniazdowski Zenon

• Warsaw School of Computer Science

Bibliografia

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