TY - GEN
T1 - Distributed data mining methodology for clustering and classification model
AU - Gorawski, Marcin
AU - Pluciennik-Psota, Ewa
PY - 2010
Y1 - 2010
N2 - Distributed computing and data mining are nowadays almost ubiquitous. Authors propose methodology of distributed data mining by combining local analytical models (built in parallel in nodes of a distributed computer system) into a global one without necessity to construct distributed version of data mining algorithm. Different combining strategies for clustering and classification are proposed and their verification methods as well. Proposed solutions were tested with data sets coming from UCI Machine Learning Repository.
AB - Distributed computing and data mining are nowadays almost ubiquitous. Authors propose methodology of distributed data mining by combining local analytical models (built in parallel in nodes of a distributed computer system) into a global one without necessity to construct distributed version of data mining algorithm. Different combining strategies for clustering and classification are proposed and their verification methods as well. Proposed solutions were tested with data sets coming from UCI Machine Learning Repository.
UR - https://www.scopus.com/pages/publications/77955393928
U2 - 10.1007/978-3-642-13208-7_41
DO - 10.1007/978-3-642-13208-7_41
M3 - Conference contribution
AN - SCOPUS:77955393928
SN - 3642132073
SN - 9783642132070
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 323
EP - 330
BT - Artificial Intelligence and Soft Computing - 10th International Conference, ICAISC 2010
T2 - 10th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2010
Y2 - 13 June 2010 through 17 June 2010
ER -