Design of ART-based hierarchical clustering algorithm using quadratic junction neural networks

Author

Gu, Ming

Author_Institution

Dept. of Software, Shenzhen Polytech., Shenzhen, China

Volume

1

fYear

2010

fDate

10-12 Dec. 2010

Firstpage

81

Lastpage

85

Abstract

In this paper, Structure and properties of neural networks with quadratic junction are presented. Unsupervised learning rules about the neural networks are given. Using this kind of neural networks, an ART-based hierarchical clustering algorithm is suggested. The algorithm can determine the number of clusters and clustering data. The time and space complexity of the algorithm are discussed. A 2-D artificial data set is used to illustrate and compare the effectiveness of the proposed algorithm and K-means algorithm.

Keywords

ART neural nets; pattern clustering; unsupervised learning; 2D artificial data set; ART based hierarchical clustering; K-means algorithm; quadratic junction neural networks; unsupervised learning rules; Algorithm design and analysis; Artificial neural networks; Clustering algorithms; Complexity theory; Junctions; Neurons; Subspace constraints; Algorithm complexity; Cluster analysis; Neural network; Unsupervised learnin;

fLanguage

English

Publisher

ieee

Conference_Titel

Progress in Informatics and Computing (PIC), 2010 IEEE International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-6788-4

Type

conf

DOI

10.1109/PIC.2010.5687396

Filename

5687396