SCALABLE SPATIOTEMPORAL CLUSTERING OF HETEROGENEOUS EVENTS

• US 20140337264A1
• Filed: 05/08/2013
• Published: 11/13/2014
• Est. Priority Date: 05/08/2013
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Bart, Evgeniy, Price, Robert R.
• Granted Patent: US 9,230,216 B2
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

One embodiment of the present invention provides a system for clustering heterogeneous events. During operation, the system finds a partition of events into clusters such that each cluster includes a set of events. In addition, the system estimates probability distributions for various properties of events associated with each cluster. The system obtains heterogeneous event data, and analyzes the heterogeneous event data to determine the distribution of event properties associated with clusters and to assign events to clusters.

Claims

1. A computer-executable method, comprising:

obtaining heterogeneous event data;

estimating a distribution of events into dusters such that each cluster includes a set of events; and

estimating a probability distribution for each event property associated with each cluster.

2. The method of claim 1, wherein a respective event property is one of:

event time, event location, event type, event description, event location properties, or event time properties.

3. The method of claim 2, wherein the event location properties indicate whether the location is urban, rural, or near or far from a road.

4. The method of claim 2, wherein the event time properties indicate whether the event time is day, night, weekend, or weekday.

5. The method of claim 1, further comprising:

analyzing the heterogeneous event data to determine the distribution of event properties associated with clusters comprises using a joint probability distribution that factorizes as follows;

6. The method of claim 1, further comprising applying a Gibbs sampling or variational inference algorithm to determine the distribution of event properties associated with clusters and to assign events to clusters.

7. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method, the method comprising:

obtaining heterogeneous event data;

estimating a distribution of events into clusters such that each cluster includes a set of events; and

estimating a probability distribution for each event property associated with each cluster.

8. The computer-readable storage medium of claim 7, wherein a respective event property is one of:

event time, event location, event type, event description, event location properties, or event time properties.

9. The computer-readable storage medium of claim 8, wherein the event location properties indicate whether the location is urban, rural, or near or far from a road.

10. The computer-readable storage medium of claim 8, wherein the event time properties indicate whether the event time is day, night, weekend, or weekday.

11. The computer-readable storage medium of claim 7, wherein the computer-readable storage medium stores additional instructions that, when executed, cause the computer to perform additional steps comprising:

analyzing the heterogeneous event data to determine the distribution of event properties associated with clusters comprises using a joint probability distribution that factorizes as follows;

12. The computer-readable storage medium of claim 7, wherein the computer-readable storage medium stores additional instructions that, when executed, cause the computer to perform additional steps comprising applying a Gibbs sampling or variational inference algorithm to determine the distribution of event properties associated with clusters and to assign events to clusters.

13. A computing system for performing a method, the system comprising:

one or more processors,

a computer-readable medium coupled to the one or more processors having instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to perform operations comprising;

obtaining heterogeneous event data;

estimating a distribution of events into clusters such that each cluster includes a set of events; and

estimating a probability distribution for each event property associated with each cluster.

14. The system of claim 13, wherein a respective event property is one of:

event time, event location, event type, event description, event location properties, or event time properties.

15. The system of claim 14, wherein the event location properties indicate whether the location is urban, rural, or near or far from a road.

16. The system of claim 14, wherein the event time properties indicate whether the event time is day, night, weekend, or weekday.

17. The system of claim 13, wherein the computer-readable storage medium stores additional instructions that, when executed, cause the computer to perform additional steps comprising:

analyzing the heterogeneous event data to determine the distribution of event properties associated with clusters comprises using a joint probability distribution that factorizes as follows;

18. The system of claim 13, wherein the computer-readable storage medium stores additional instructions that, when executed, cause the computer to perform additional steps comprising:

applying a Gibbs sampling or variational inference algorithm to determine the distribution of event properties associated with clusters and to assign events to clusters.