Many real world applications require online analysis of streaming data, making an adaptive clustering technique desirable. Most adaptive variations of available clustering techniques are application-specific, and do not apply to the applications of clustering as a whole. With this in mind, a generalized algorithm is proposed which is a modification of the fuzzy c-means clustering technique, so that dynamic data environments in differing fields can be addressed and analyzed. We demonstrate the capabilities of the dynamic fuzzy c-means (dFCM) algorithm with the aid of synthetic data sets, and discuss a possible application of the dFCM algorithm in associative memories, through preliminary experiments.