Empirical Evaluation of OCLUS and GenRandomClust Algorithms of Generating Cluster Structures

• Autorzy: Jerzy Korzeniewski
• Języki publikacji: EN

Abstrakty

EN

The OCLUS algorithm and genRandomClust algorithm are newest proposals of generating multivariate cluster structures. Both methods have the capacity of controlling cluster overlap, but both do it quite differently. It seems that OCLUS method has much easier, intuitive interpretation. In order to verify this opinion a comparative assessment of both algorithms was carried out. For both methods multiple cluster structures were generated and each of them was grouped into the proper number of clusters using &-means. The groupings were assessed by means of divisions similarity index (modified Rand index) referring to the classification resulting from the generation. The comparison criterion is the behaviour of the overlap parameters of structures. The monotonicity of the overlap parameters with respect to the similarity index is assessed as well as the variability of the similarity index for the fixed value of overlap parameters. Moreover, particular attention is given to checking the existence of an overlap parameter limit for the classical grouping procedures as well as uniform nature of overlap control with respect to all clusters. (original abstract)

Słowa kluczowe

EN

Cluster analysis; Algorithms; Data Mining; Mathematical statistics

PL

Analiza skupień; Algorytmy; Data Mining; Statystyka matematyczna

• Czasopismo: Statistics in Transition
• Rocznik: 2013
• Tom: 14
• Numer: nr 3
• Strony: 487--494

Twórcy

autor

Jerzy Korzeniewski

• University of Lodz, Poland

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