Copasetic clustering: Making sense of large-scale images

In an information rich world, the task of data analysis is becoming ever more complex. Even with the processing capability of modern technology, more often than not, important details become saturated and thus, lost amongst the volume of data. With analysis problems ranging from discovering credit card fraud to tracking terrorist activities the phrase "a needle in a haystack" has never been more apt. In order to deal with large data sets current approaches require that the data be sampled or summarised before true analysis can take place. In this paper we propose a novel pyramidic method, namely, copasetic clustering, which focuses on the problem of applying traditional clustering techniques to large-scale data sets while using limited resources. A further benefit of the technique is the transparency into intermediate clustering steps; when applied to spatial data sets this allows the capture of contextual information. The abilities of this technique are demonstrated using both synthetic and biological data.