Analytical models combining methodology with classification model example
Distributed computing is nowadays almost ubiquities. So is data mining - time and hardware resources consuming process of building analytical models of data. Authors propose methodology of combining local analytical models (build parallely in nodes of distributed computer system) into a global one w...
Saved in:
Published in | 2008 1st International Conference on Information Technology pp. 1 - 4 |
---|---|
Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.05.2008
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Distributed computing is nowadays almost ubiquities. So is data mining - time and hardware resources consuming process of building analytical models of data. Authors propose methodology of combining local analytical models (build parallely in nodes of distributed computer system) into a global one without necessary to construct distributed version of data mining algorithm. Basic assumptions for proposed solution is (i) a complete horizontal data fragmentation and (ii) a model form understood for human being. All steps of combining methodology are presented with classification model example in form of a rule set. Authors define and consider problems with combining local classification modelspsila rules into one final set of global model rules encompassing conflicting rules, sub-rules, partial sub-rules and unclassified objects. Algorithms for different combining strategies are also presented as well as their tests results. Tests were conducted with data sets from UCI Machine Learning Repository. |
---|---|
ISBN: | 9781424422449 1424422442 |
DOI: | 10.1109/INFTECH.2008.4621623 |