Patent · US Active

Massive clustering of discrete distributions

US9720998B2 · kind B2 · utility

38Cited by

4References

17Claims

0Family size

Assignee

The Penn State Research Foundation · US

Inventors

James Z. Wang · State College, US
Yu Zhang · Shanghai, CN
Jia Li · State College, US

Key dates

Filing date	Nov 15, 2013
Grant date	Aug 1, 2017
Priority date	—
Expiry date	Mar 21, 2035

Classification

Technology area (CPC G)Physics
CPC primaryG06F16/285
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

The trend of analyzing big data in artificial intelligence requires more scalable machine learning algorithms, among which clustering is a fundamental and arguably the most widely applied method. To extend the applications of regular vector-based clustering algorithms, the Discrete Distribution (D2) clustering algorithm has been developed for clustering bags of weighted vectors which are well adopted in many emerging machine learning applications. The high computational complexity of D2-clustering limits its impact in solving massive learning problems. Here we present a parallel D2-clustering algorithm with substantially improved scalability. We develop a hierarchical structure for parallel computing in order to achieve a balance between the individual-node computation and the integration process of the algorithm. The parallel algorithm achieves significant speed-up with minor accuracy loss.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.