Parallelization of the K-means fast learning artificial neural network

Author

Shilpa, Noogala Boopal ; Phuan, Alex Tay Leng

Author_Institution

Nanyang Technol. Univ., Singapore

Volume

2

fYear

2003

fDate

15-18 Dec. 2003

Firstpage

994

Abstract

The paper presents a parallelization study of an improved K-means fast learning artificial neural network (K-FLANN) within the parallel virtual machine (PVM) environment. The study discusses the improvements made on the K-FLANN II algorithm, which eventually lead to a consistent set of cluster centroids, regardless of the data presentation sequence DPS. To further improve clustering efficiency, a form of hierarchical clustering is explored, leading to the parallel-distributed implementation of the K-FLANN. Results of the investigation are presented along with a discussion of the fundamental behavior of the parallel network.

Keywords

learning (artificial intelligence); neural net architecture; parallel architectures; parallel machines; pattern clustering; virtual machines; DPS; K-FLANN; K-means fast learning artificial neural network; PVM environment; cluster centroid; data presentation sequence; hierarchical clustering; parallel virtual machine; Artificial neural networks; Clustering algorithms; Concurrent computing; Equations; Joining processes; Machine learning; Neurons; Notice of Violation; Paper technology; Virtual machining;

fLanguage

English

Publisher

ieee

Conference_Titel

Information, Communications and Signal Processing, 2003 and Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference on

Print_ISBN

0-7803-8185-8

Type

conf

DOI

10.1109/ICICS.2003.1292608

Filename

1292608