A History of Cluster Analysis Using the Classification Society's Bibliography Over Four Decades

The Classification Literature Automated Search Service, an annual bibliography based on citation of one or more of a set of around 80 book or journal publications, ran from 1972 to 2012. We analyze here the years 1994 to 2011. The Classification Society's Service, as it was termed, has been produced by the Classification Society. In earlier decades it was distributed as a diskette or CD with the Journal of Classification. Among our findings are the following: an enormous increase in scholarly production post approximately 2000; a very major increase in quantity, coupled with work in different disciplines, from approximately 2004; and a major shift also from cluster analysis in earlier times having mathematics and psychology as disciplines of the journals published in, and affiliations of authors, contrasted with, in more recent times, a "centre of gravity" in management and engineering.Comment: 23 pages, 9 figure

Towards an online mitigation strategy for N2O emissions through principal components analysis and clustering techniques

Emission of N2O represents an increasing concern in wastewater treatment, in particular for its large contribution to the plant's carbon footprint (CFP). In view of the potential introduction of more stringent regulations regarding wastewater treatment plants' CFP, there is a growing need for advanced monitoring with online implementation of mitigation strategies for N2O emissions. Mechanistic kinetic modelling in full-scale applications, are often represented by a very detailed representation of the biological mechanisms resulting in an elevated uncertainty on the many parameters used while limited by a poor representation of hydrodynamics. This is particularly true for current N2O kinetic models. In this paper, a possible full-scale implementation of a data mining approach linking plant-specific dynamics to N2O production is proposed. A data mining approach was tested on full-scale data along with different clustering techniques to identify process criticalities. The algorithm was designed to provide an applicable solution for full-scale plants' control logics aimed at online N2O emission mitigation. Results show the ability of the algorithm to isolate specific N2O emission pathways, and highlight possible solutions towards emission control