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A unique characteristic of the Hunch Engine™ is that the user does not need to have any knowledge of the techniques and operations that are used within the specific application. By selecting "interesting" candidates through the HE interface, the user is implicitly determining which of potentially many search filters and what parameter settings to use. In one example we developed a tool to help a user identify clusters in a large data set. For instance, one may wish to identify customer segments based on extensive consumer data. Currently, this process requires the user to be familiar with data mining techniques such as dimension reduction and clustering. The figure below shows how HE can be used to discover segments even with no knowledge of data mining. Starting with a high-dimensional data set, the user is presented with several candidates, each corresponding to a particular 2-D representation of the data (left panel).
As the user selects candidates that seem to contain some information, the Interactive Data Mining tool begins to reveal some structure in the data (center panel). Eventually (right panel), the user reaches solutions that show clear existence of five distinct clusters in the data. This simple demo could easily be extended to use a large variety of data mining algorithms, without requiring the user to have any understanding of the underlying techniques. In addition, even expert users benefit from this tool, because it allows them to search a vast space of algorithms, rather than being forced to choose a class of algorithms at the outset -- a practice that often limits the nature and extent of the search. |
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