Publications

Following Ramakrishan and J. Gehrke, 2nd ed, 2000, "Data mining consists of finding interesting trends or patterns in large datasets, in order to guide decisions about future activities." Sometimes, a trend or pattern in a dataset can be observed on much smaller data-subset, which in some sense distinguishes itself better than any other thinkable data-subset. Unfortunately, this simple idea leads to non-scalable algorithms, when, in general, the running time to find the best data-subset grows in proportion to the number of all thinkable data-subsets, so-called exponential NP-hard time table. Monotonic Systems - the MS-framework is a kind of technique, which ensures the algorithms scalability on the expense of monotonic constraint upon data-subsets "goodness criteria"; hereby the technique inherits its name.

An example of MS-framework implementation on Survey Datasets, which we hope is understandable for the general audience, may be downloaded at

http://www.datalaundering.com/download/cleaning.pdf.

However, for those, who are interested in mathematical purity, a series of articles on much higher level of abstraction is available for download as well:

http://www.datalaundering.com/monotone.htm or http://www.datalaundering.com/furtherd.htm .