Classification into homogeneous groups using combined cluster and discriminant analysis
2014
Kovács, József | Kovács, Solt | Magyar, Norbert | Tanos, Péter | Hatvani, István Gábor | Anda, Angéla
The classification of observations into groups is a general procedure in modern research. However, when searching for homogeneous groups the difficulty of deciding whether further division of a classification is necessary or not to obtain the desired homogeneous groups arises. The presented method, Combined cluster and discriminant analysis (CCDA), aims to facilitate this decision.CCDA consists of three main steps: (I) a basic grouping procedure; (II) a core cycle where the goodness of preconceived and random classifications is determined; and (III) an evaluation step where a decision has to be made regarding division into sub-groups. These steps of the proposed method were implemented in R in a package, under the name of ccda.To present the applicability of the method, a case study on the water quality samples of Neusiedler See is presented, in which CCDA classified the 33 original sampling locations into 17 homogeneous groups, which could provide a starting point for a later recalibration of the lake's monitoring network.
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