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- W2752670911 abstract "Traditional methods for cluster analysis can beinappropriate to current necessities if not considering distributeddata sets. Self-organizing maps (SOM) have been widely used as asoftware tool for visualization of high-dimensional data. Importantcharacteristics of SOM include information compression andmaintenance of data topology. This work presents strategies forefficient clustering analysis in geographically distributeddatabases using self-organizing maps. Local data sets (relative tovertical partitions of the database) are applied to distinct mapsin order to obtain partial visions of the existing clusters.Further, units of each local map are chosen to represent originaldata and sent to a central site, which perform a joint of thepartial results. Experimental results of the application of thisstrategy in different data sets are presented." @default.
- W2752670911 created "2017-09-15" @default.
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- W2752670911 date "2008-06-06" @default.
- W2752670911 modified "2023-10-17" @default.
- W2752670911 title "PARTSOM: AN ARCHITECTURE FOR DISTRIBUTED DATA CLUSTERINGBASED ON MULTIPLE SELF-ORGANIZING MAPS" @default.
- W2752670911 doi "https://doi.org/10.4301/contecsi9969320081203" @default.
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