Text Processing by Using Projective ART Neural Networks

被引：0

作者：

Forgac, Radoslav ^{[1
]}

Krakovsky, Roman ^{[2
]}

机构：

[1] Slovak Acad Sci, Inst Informat, Dept Parallel & Distributed Informat Proc, Bratislava, Slovakia

[2] Catholic Univ, Fac Educ, Dept Informat, Ruzomberok, Slovakia

来源：

2016 NEW TRENDS IN SIGNAL PROCESSING (NTSP) | 2016年

关键词：

Projective Adaptive Resonance Theory; neural network; clustering; text processing;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents the summary of experience obtained with the modified clustering algorithm of Projective Adaptive Resonance Theory. The algorithm was proposed by authors, and was tested for text processing. Possible usage of the algorithm is exemplified by text document clustering, and generation of keyword dictionaries from text documents.

引用

页码：29 / 33

页数：5

共 20 条

[1]

Aggarwal CC, 2014, CH CRC DATA MIN KNOW, P1

[2]

Aggarwal CC, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P61, DOI 10.1145/304181.304188

[3]

[Anonymous], 2004, ACM SIGKDD EXPLOR NE

[4]

Baeza-Yates R, 1999, MODERN INFORM RETRIE, V463

[5]

Baker K., 2005, The Ohio State University, V2005, P1

[6] Dynamics of projective adaptive resonance theory model: The foundation of PART algorithm [J].

Cao, YQ ;

Wu, JH .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2004, 15 (02) :245-260

[7] Projective ART for clustering data sets in high dimensional spaces [J].

Cao, YQ ;

Wu, JH .

NEURAL NETWORKS, 2002, 15 (01) :105-120

[8]

Forgac R., INES 2015 IEEE 19 IN, P421

[9]

Grossberg S., 2002, ADAPTIVE RESONANCE T

[10]

Han J., 2006, Data mining: Concepts and Techniques

← 1 2 →