A kombinált klaszter- és diszkriminanciaanalízis (CCDA) adatelemző módszer alkalmazása földtudományi feladatok megoldására

Application of the Combined cluster and discriminant analysis (CCDA) data analysis method in solving earth-science tasks


  • KOVÁCS József


optimális, homogén, csoportosítás


The grouping of variables/sampling sites/events etc. is a frequent task in modern research. When applied, the question needed to be answered is: How to determine groups with not only similar but homogeneous elements? Combined cluster and discriminant analysis (CCDA) is a new technique that combines two traditional methods to determine the optimal number of homogeneous groups in an objective way. A software applying CCDA was also developed, which can be used under any operating system supporting R (http://cran.r-project.org/). To demonstrate the applicability of the method, several research results are presented from numerous fields of Earth Sciences. The present paper mainly focuses on two examples: 1. the determination of optimal groups of the karst water spring in Budapest, which clustered springs and wells with the same hydrogeological background; and 2. the classification of Lake Balaton's water quality sampling sites into homogeneous groups, which can significantly help the subsequent recalibration of the lake's monitoring network in the future


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NOVÁK, M., PALYA, D., BODAI, ZS., NYIRI, Z., MAGYAR, N., KOVÁCS, J., EKE, ZS., 2017: Combined Cluster and Discriminant Analysis an efficient Chemometric Approach in Diesel Fuel Characterization. Forensic Science International 270, 61–69.