1. A computer-implemented method, comprising: generating a core group of clusters of objects; receiving, by a server computer, the core group of clusters of objects, wherein each object of the objects 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 of objects is clustered based on the dimension k; wherein the generating the core group of clusters of objects 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, by the server computer during a particular time window, a multi-dimensional stream of data points representing the objects of the core group of clusters, each data point of the data points respectively representing an instance of a dimension k describing a feature of an object within the group of objects; and for said each data point of the data points, adding, by the server computer, 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, by the server computer, properties of the first cluster of objects in response to the adding the object, wherein the updating the properties includes calculating a first standard deviation of clustering dimension k for the first cluster of objects; in response to receiving a request via network for core cluster information, determining, by the server computer, whether to update the core group of clusters using the updated properties of the first cluster of objects, wherein the determining whether to update the core group of clusters of objects comprises: comparing the first standard deviation of clustering dimension k to a minimum standard deviation of clustering dimension k and updating, by the server computer, the core group of clusters of objects based on the tuning parameter representing clustering density.