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Volumn 2000-January, Issue , 2000, Pages 661-665

Optimizing the parSOM neural network implementation for data mining with distributed memory systems and cluster computing

Author keywords

Application software; Clustering algorithms; Computer architecture; Computer networks; Data analysis; Data mining; Delay; Neural networks; Software libraries; Time series analysis

Indexed keywords

APPLICATION PROGRAMS; CLUSTER COMPUTING; CLUSTERING ALGORITHMS; COMPUTER ARCHITECTURE; COMPUTER NETWORKS; COMPUTER SOFTWARE; CONFORMAL MAPPING; DATA HANDLING; DATA MINING; DATA REDUCTION; EXPERT SYSTEMS; INFORMATION ANALYSIS; INPUT OUTPUT PROGRAMS; MEMORY ARCHITECTURE; NEURAL NETWORKS; OPTIMIZATION; SELF ORGANIZING MAPS; TIME SERIES ANALYSIS;

EID: 3042725428     PISSN: 15294188     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DEXA.2000.875094     Document Type: Conference Paper
Times cited : (15)

References (10)
  • 8
    • 0242453405 scopus 로고    scopus 로고
    • Using self-organizing maps to organize document collections and to characterize subject matters
    • Florence, Italy
    • A. Rauber and D. Merkl. Using self-organizing maps to organize document collections and to characterize subject matters. In Proc. 10th Conf. on Database and Expert Systems Applications, Florence, Italy, 1999.
    • (1999) Proc. 10th Conf. on Database and Expert Systems Applications
    • Rauber, A.1    Merkl, D.2
  • 10
    • 25844504969 scopus 로고    scopus 로고
    • parSOM: Using parallelism to overcome memory latency in self-organizing neural networks
    • P. Tomsich, A. Rauber, and D. Merkl. parSOM: Using parallelism to overcome memory latency in self-organizing neural networks. In High Performance Computing and Networking, 2000.
    • (2000) High Performance Computing and Networking
    • Tomsich, P.1    Rauber, A.2    Merkl, D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.