1. A computer-implemented method, comprising: receiving a core group of clusters of objects, wherein each object is represented by a corresponding instance of a multi-dimensional feature vector including a dimension k, wherein k is a number, wherein the core group of clusters is clustered based on the dimension k; and wherein generating the core group of clusters is based in part on at least a tuning parameter representing one or more of clustering density, clustering distance, and a clustering standard deviation; receiving a multi-dimensional stream of data points representing the objects of the core group of clusters, each data point respectively representing an instance of dimension k describing a feature of an object within the group of objects; and for each data point, adding an object described by the data point to a first cluster of objects within the core group of clusters in response to classifying the object as belonging to the first cluster of objects; updating properties of the first cluster of objects in response to adding the object, wherein updating the properties includes calculating a first standard deviation of clustering dimension k for the first cluster of objects; and determining, by a processor, whether to update the core group of clusters using the updated properties of the first cluster of objects.