Current clustering techniques are able to identify arbitrarily shaped clusters in the presence of noise, but depend on carefully chosen model parameters. The choice of model param...
Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clusteri...
This paper proposes an efficient technique for partitioning large biometric database during identification. In this technique feature vector which comprises of global and local de...
Hunny Mehrotra, Dakshina Ranjan Kisku, V. Bhawani ...
Clusters have made the jump from lab prototypes to fullfledged production computing platforms. The number, variety, and specialized configurations of these machines are increasi...
— We propose a randomized data mining method that finds clusters of spatially overlapping images. The core of the method relies on the min-Hash algorithm for fast detection of p...