By Mohammadreza Khanmohammadi
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Müller, K. R. J. Chem. Inf. Model. 49 (2009) 2077-2081. , Schwab, C. , Gasteiger, J. com, Erlangen, Germany, 2004. , Dehmer, M. Chemom. Intell. Lab. Syst. 117 (2012) 31-41.  Mevik, B. , Wehrens, R. J. Stat. Software 18 (2007) 1-24. In: Current Applications of Chemometrics Editor: Mohammadreza Khanmohammadi ISBN: 978-1-63463-117-4 © 2015 Nova Science Publishers, Inc. Chapter 3 FUZZY CLUSTERING OF ENVIRONMENTAL DATA Costel Sârbu Faculty of Chemistry and Chemical Engineering, Babeş-Bolyai University, Cluj-Napoca, Romania ABSTRACT Cluster analysis is a large field, both within fuzzy sets and beyond it.
No 11, RO-400028, Cluj-Napoca, Romania. ro. 34 Costel Sârbu INTRODUCTION Imprecision in data and information gathered from measurements and observations about our universe is either statistical or nonstatistical. This latter type of uncertainty is called fuzziness. We all assimilate and use fuzzy data, vague rules, and imprecise information, just as we are able to make decisions about situations that seems to be governed by an element of chance. Accordingly, computational models of real systems should also be able to recognize, represent, manipulate, interpret, and use both fuzzy and statistical uncertainties.
Data are from n = 6458 chemical structures with the mutagenicity determined by AMES tests as collected and provided by Katja Hansen (University of Technology, Berlin, Germany, 2009) . 30 Kurt Varmuza and Peter Filzmoser Class 1 contains 2970 not mutagenic compounds, class 2 contains 3488 mutagenic compounds. 0  from approximated 3D structures and all H-atoms explicitly given (calculated by software Corina ). This data set for binary classification has been described and used previously [30, 34].