DocumentCode :
2957571
Title :
High performance clustering for large data warehouses using peer-to-peer genetic algorithm
Author :
Shah, M. Nauman ; Mahmood, Rafia
Author_Institution :
Nat. Univ. of Comput. & Emerging Sci., FAST-NU, Islamabad, Pakistan
fYear :
2003
fDate :
8-9 Dec. 2003
Firstpage :
420
Lastpage :
423
Abstract :
High volumes of data pose a challenge to the scalability of data mining algorithms. Dividing this data into equal partitions and processing it in parallel naturally becomes a choice. Peer-to-peer computing exposes a bright source for exploiting parallelism and maintaining scale-up capability. We consider parallelism in genetic algorithms while computing the fitness of the population individuals (chromosomes). This strategy has an edge over its counterpart, that is, parallelism in genetic operators, because genetic operators tend to be computationally cheap. Simply speaking this scheme supports large data sets, that is. larger the data size, larger will be the degree of parallelism achieved.
Keywords :
data mining; data warehouses; genetic algorithms; parallel algorithms; pattern clustering; peer-to-peer computing; chromosomes; data mining; genetic algorithm; high performance clustering; large data warehouses; parallel algorithms; peer-to-peer computing; population fitness; scalability; Biological cells; Clustering algorithms; Concurrent computing; Data mining; Data warehouses; Genetic algorithms; Parallel processing; Partitioning algorithms; Peer to peer computing; Scalability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multi Topic Conference, 2003. INMIC 2003. 7th International
Print_ISBN :
0-7803-8183-1
Type :
conf
DOI :
10.1109/INMIC.2003.1416762
Filename :
1416762
Link To Document :
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