Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for modeling multi-dimensional, dynamically evolving data using dynamic clustering. In one aspect, a method includes receiving a core group of clusters of objects, each object being represented by a corresponding instance of a multi-dimensional feature vector including a dimension k; receiving a stream of data points representing a group of objects, 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; updating properties of the first cluster of objects in response to adding the object; and determining whether to update the core group of clusters using the updated properties of the first cluster of objects.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for modeling multi-dimensional, dynamically evolving data using dynamic clustering. In one aspect, a method includes receiving a core group of clusters of objects, each object being represented by a corresponding instance of a multi-dimensional feature vector including a dimension k; receiving a stream of data points representing a group of objects, 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; updating properties of the first cluster of objects in response to adding the object; and determining whether to update the core group of clusters using the updated properties of the first cluster of objects.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media for modeling multi-dimensional, dynamically evolving data using dynamic clustering. In one aspect, a method includes receiving a core group of clusters of objects, each object being represented by a corresponding instance of a multi-dimensional feature vector including a dimension k; receiving a stream of data points representing a group of objects, 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; updating properties of the first cluster of objects in response to adding the object; and determining whether to update the core group of clusters using the updated properties of the first cluster of objects.