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Many data sets can be viewed as a collection of samples representing mixtures of a relatively small number of end members. When end members are present in the sample set, the algorithm QMODEL by Klovan and Miesch can efficiently determine proportionate contributions. EXTENDED QMODEL by Full, Ehrlich, and Klovan was designed to deduce the composition of realistic end members when the end members are not represented by samples. However, in the presence of high levels of random variation or outliers not belonging to the system of interest, EXTENDED QMODEL may not be reliable inasmuch as it is largely dependent on extreme values for definition of an initial mixing polyhedron. FUZZY QMODEL utilizes the fuzzy c-means algorithm of Bezdek to provide an alternative initial mixing polyhedron. This algorithm utilizes the collective property of all the data rather than outliers and so can produce suitable solutions in the presence of noisy or messy data points. 相似文献
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Analysis of empirical data considered to be mixtures of a finite number of end members has been a topic of increasing interest recently. The algorithms EXTENDED CABFAC and QMODEL by Klovan and Miesch (1976) represent a satisfactory solution to this problem if pure end members are captured within the data set or if the composition of “true” end members are known a priori. Where neither condition is satisfied, the composition of “external” end members can, under certain conditions, be deduced from the structure of the data. Described herein is an algorithm termed EXTENDED QMODEL which defines feasible end members which are “closest” to the data envelope. 相似文献
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