  Data mining algorithms are originally designed by assuming the data is
available at one centralized site.These algorithms also assume that the whole
data is fit into main memory while running the algorithm. But in today's
scenario the data has to be handled is distributed even geographically.
Bringing the data into a centralized site is a bottleneck in terms of the
bandwidth when compared with the size of the data. In this paper for multiclass
SVM we propose an algorithm which builds a global SVM model by merging the
local SVMs using a distributed approach(DSVM). And the global SVM will be
communicated to each site and made it available for further classification. The
experimental analysis has shown promising results with better accuracy when
compared with both the centralized and ensemble method. The time complexity is
also reduced drastically because of the parallel construction of local SVMs.
The experiments are conducted by considering the data sets of size 100s to
hundred of 100s which also addresses the issue of scalability.
