Influence of distance measure choice on the results of hierarchical clustering

Hierarchical clustering method is used to assign observations into clusters which are 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 distinct 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’ offers ways to program new modules in an efficient manner so it was used for implementation, testing, data processing and result visualization. Finally, a comparison was made between results obtained by previously available widget and output of newly programmed widget which employed new variable types and new distance measure.