Editing Granular computing (section)

====Component granulation====
Another perspective on concept granulation may be obtained from work on parametric models of categories.  In [[mixture model]] learning, for example, a set of data is explained as a mixture of distinct [[Gaussian distribution|Gaussian]] (or other) distributions.  Thus, a large amount of data is "replaced" by a small number of distributions.  The choice of the number of these distributions, and their size, can again be viewed as a problem of ''concept granulation''.  In general, a better fit to the data is obtained by a larger number of distributions or parameters, but in order to extract meaningful patterns, it is necessary to constrain the number of distributions, thus deliberately  ''coarsening'' the concept resolution.  Finding the "right" concept resolution is a tricky problem for which many methods have been proposed (e.g., [[Akaike information criterion|AIC]], [[Bayesian information criterion|BIC]], [[Minimum description length|MDL]], etc.), and these are frequently  considered under the rubric of "[[model regularization]]".