Izvorni znanstveni članak
Improvement of Hierarchical Clustering Results by Refinement of Variable Types and Distance Measures
Sofija Pinjušić Ćurić
; Private School of Economics and Computing, Budakova 1D, HR-10000, Zagreb, Croatia
Mihaela Vranić
orcid.org/0000-0003-0005-831X
; University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Fundamentals of Electrical Engineering and Measurements, Unska 3, HR-10000, Zagreb, Croatia
Damir Pintar
orcid.org/0000-0001-9589-7890
; University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Fundamentals of Electrical Engineering and Measurements, Unska 3, HR-10000, Zagreb, Croatia
Sažetak
Hierarchical clustering method is used to assign observations into clusters further connected to form a hierarchical structure. Observations in the same cluster are close together according to the predetermined distance measure, while observations belonging to different clusters are afar. This paper presents an implementation of specific distance measure used to calculate distances between observations which are described by a mixture of variable types. Data mining tool ‘Orange’ was used for implementation, testing, data processing and result visualization. Finally, a comparison was made between results obtained by using already available widget and the output of newly programmed widget which employs new variable types and new distance measure. The comparison was made on different well-known datasets.
Ključne riječi
Hierarchical clustering; Distance measure; Variable types; Dendrogram
Hrčak ID:
78302
URI
Datum izdavanja:
6.3.2012.
Posjeta: 2.421 *