Dynamic Clustering For Streaming Data

An apparatus comprising at least one processor and at least one memory for storing instructions that, with the at least one processor, cause the apparatus to:

• for each data point of a multi-dimensional stream of data points, the multi-dimensional stream of data points representing objects of a core group of clusters of objects, each data point respectively representing an instance of a first dimension describing a feature of an object within the objects, wherein the core group of clusters of objects is generated based at least in part on a tuning parameter,
• add an object described by the data point to a first cluster of objects within the core group of clusters of objects in response to classifying the object as belonging to the first cluster of objects;
• update properties of the first cluster of objects in response to adding the object, wherein updating the properties of the first cluster of objects comprises calculating a first standard deviation of the first cluster of objects for the first dimension;
• and
• in response to receiving a request for core cluster information, determine whether to update the core group of clusters using the updated properties of the first cluster of objects, wherein determining whether to update the core group of clusters comprises
• comparing the first standard deviation to a minimum standard deviation for the first dimension;
• and
• updating the core group of clusters of objects based on the comparison and the tuning parameter.

A computer program product comprising a computer readable medium storing instructions that, when executed by a processor, cause an apparatus to:

• for each data point of a multi-dimensional stream of data points, the multi-dimensional stream of data points representing objects of a core group of clusters of objects, each data point respectively representing an instance of a first dimension describing a feature of an object within the objects, wherein the core group of clusters of objects is generated based at least in part on a tuning parameter,
• add an object described by the data point to a first cluster of objects within the core group of clusters of objects in response to classifying the object as belonging to the first cluster of objects;
• update properties of the first cluster of objects in response to adding the object, wherein updating the properties of the first cluster of objects comprises calculating a first standard deviation of the first cluster of objects for the first dimension;
• and
• in response to receiving a request for core cluster information, determine whether to update the core group of clusters using the updated properties of the first cluster of objects, wherein determining whether to update the core group of clusters comprises
• comparing the first standard deviation to a minimum standard deviation for the first dimension;
• and
• updating the core group of clusters of objects based on the comparison and the tuning parameter.

A computer implemented method, comprising:

• for each data point of a multi-dimensional stream of data points, the multi-dimensional stream of data points representing objects of a core group of clusters of objects, each data point respectively representing an instance of a first dimension describing a feature of an object within the objects, wherein the core group of clusters of objects is generated based at least in part on a tuning parameter,
• adding an object described by the data point to a first cluster of objects within the core group of clusters of objects 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 of the first cluster of objects comprises calculating a first standard deviation of the first cluster of objects for the first dimension;
• and
• in response to receiving a request for core cluster information, determining whether to update the core group of clusters using the updated properties of the first cluster of objects, wherein determining whether to update the core group of clusters comprises
• comparing the first standard deviation to a minimum standard deviation for the first dimension;
• and
• updating the core group of clusters of objects based on the comparison and the tuning parameter.