Peano Count Model (PCM) (RFT-75)
Standard data mining techniques have been sufficient for some areas of information analysis where the datasets are small enough that analysis can be performed relatively quickly and efficiently. However, these standard data mining techniques, such as association rule mining (ARM), have not been as successful in areas such as bioinformatics, nanotechnology, VLSI design, and spatial data, which each have extremely large data sets and where mining implicit relationships among the data can be prohibitively time-consuming.
These NDSU-developed data mining technologies are designed specifically for organizing extremely large datasets into an efficiently usable form. The organizational format of the data takes into account the fact that different bits of data can have different degrees of contribution to value. For example, in some applications, high-order bits alone may provide the necessary information for data mining, making the retention of all data unnecessary. The organizational format used also recognizes the need to facilitate the representation of a precision hierarchy. That is, a band may be well represented by a single bit or may require eight bits to be appropriately represented.
Henry Nowak, Technology Manager
RFT, 75, RFT75
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