Authors:
Jung Song Lee
;
Soon Cheol Park
;
Jong Joo Lee
and
Han Heeh Ham
Affiliation:
Chonbuk National University, Korea, Republic of
Keyword(s):
Document Clustering, Genetic Algorithms, Multi-Objective Genetic Algorithms, GPGPU, CUDA.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Distributed Control Systems
;
Enterprise Information Systems
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge-Based Systems Applications
;
Soft Computing
Abstract:
In this paper, we propose a method of enhancing Multi-Objective Genetic Algorithms (MOGAs) for document clustering with parallel programming. The document clustering using MOGAs shows better performance than other clustering algorithms. However, the overall computation time of the MOGAs is considerably long as the number of documents increases. To effectively avoid this problem, we implement the MOGAs with General-Purpose computing on Graphics Processing Units (GPGPU) to compute the document similarities for the clustering. Furthermore, we introduce two thread architectures (Term-Threads and Document-Threads) in the CUDA (Compute Unified Device Architecture) language. The experimental results show that the parallel MOGAs with CUDA are tremendously faster than the general MOGAs.