Histogram methods for scientific curve classification
James R. Parker · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
Scientific data is frequently classified by using a presumed underlying model. A best fit approach can produce a set of residual values, and the minimum residual gives the classification. What is suggested here is a more visual approach - a characterization of the shape of the input curve, and a comparison against the shapes of the model histograms to collect gross shape information of various types. The example under consideration is that of respirogram curves, data collected from wastewater treatment plants, but the method applies to many other data acquisition processes.