Adapting the Weka data mining toolkit to a grid based environment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3528 LNAI, Issue , 2005, Pages 492-497

  Pérez, Maria S ^a   Sánchez, Alberto ^a   Herrero, Pilar ^a   Robles, Victor ^a   Peña, José M ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Data Mining; Data Mining Grid; Grid Services; Weka Tool

Indexed keywords

COMPUTER NETWORKS; COMPUTER SCIENCE; DATA REDUCTION; INDUSTRIAL APPLICATIONS;

DATA MINING GRID; GRID COMPUTING; GRID SERVICES; WEKA TOOL;

DATA MINING;

EID: 26944487959     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11495772_77     Document Type: Conference Paper

References (11)

1. Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining association rules between sets of items in large databases. In The 1993 ACM SIGMOD International Conference on Management of Data, 1993.
2. W. Allcock, J. Bester, A. Bresnahan, A. Chervenak, L. Liming, and S. Tuecke. GridFTP: Protocol extensions to FTP for the Grid. Global Grid Forum Draft, 2001.
3. Mario Cannataro and Domenico Talia. The knowledge grid. Commun. ACM, 46(1):89-93, 2003.
4. I. Foster and C. Kesselman, editors. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, 1999.
5. Ian Foster. The anatomy of the Grid: Enabling scalable virtual organizations. Lecture Notes in Computer Science, 2150, 2001.
6. N. Giannadakis, A. Rowe, M. Ghanem, and Y. Guo. InfoGrid: providing information integration for knowledge discovery. Information Sciences. Special Issue: Knowledge Discovery from Distributed Information Sources, 155(3-4):199-226, October 2003.
7. Rinat Khoussainov, Xin Zuo, and Nicholas Kushmerick. Grid-enabled Weka: A toolkit for machine learning on the grid. ERCIM News, 59, October 2004.
8. William A. Maniatty and Mohammed J. Zaki. A requirements analysis for parallel kdd systems. In Jose Rolim et al., editor, 3rd IPDPS Workshop on High Performance Data Mining, pages 358-265, May 2000.
9. Maria S. Pérez, Ramón A. Pons, Félix Garcia, Jesús Carretero, and María L. Córdoba. An optimization of Apriori algorithm through the usage of parallel I/O and hints. Rough Sets and Current Trends in Computing (LNAI 2475), October 2002.
10. Alberto Sánchez, José M. Peña Sánchez, María S. Pérez, Victor Robles, and Pilar Herrero. Improving distributed data mining techniques by means of a grid infrastructure. In Robert Meersman, Zahir Tari, and Angelo Corsaro, editors, OTM Workshops, volume 3292 of Lecture Notes in Computer Science, pages 111-122. Springer, 2004.
11. H. Witten and Eibe Frank. Data Mining: Practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco, 2000.