Swarna Kamal Paul

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Universal search is the asymptotically fastest method to solve a wide class of inversion problems. Recently this method has been used to develop several efficient optimal general problem solvers. However the huge constant slowdown factor associated with this algorithm prevents it from widespread practical application. Our endeavor is to reduce this constant(More)
In this paper we propose an algorithm which can identify varied shaped clusters from wide variety of input dataset with high degree of accuracy in presence of noise. The initial data processing module adopts a novel approach of Artificial Immune system to reduce data redundancy while preserving the original data patterns. The clustering module pursues a(More)
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