To find the optimal path by interacting with multiple robots is the main research area in field of robotics. The task is to find the global optimal path with a minimum amount of computation time. Path planning has numerous application... more
To find the optimal path by interacting with multiple robots is the main research area in field of robotics. The task is to find the global optimal path with a minimum amount of computation time. Path planning has numerous application like industrial robotics, to design autonomous system etc. In this paper, we survey on three most recent algorithms namely Bacteria forging Optimization (BFO), Ant Colony Optimization (ACO), Particle Swam Optimization (PSO) that can apply on multiple robots to find the optimal path. The main feature of BFO is the chemotactic movement of a virtual bacterium that is helpful to investigate the all the possible path and finally arrived at optimal solution. In Ant Colony System (ACS) algorithm is integration of heuristic and visibility equation of state transition rules for finding the optimal path. PSO is stochastic optimization technique inspired by social behaviour of bird flocking. The solutions, called particles, fly through the problem space by some set of the rules.
Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data. Among various kernel-based clustering algorithms, kernel k-means has gained popularity due to its simple iterative nature and ease... more
Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data. Among various kernel-based clustering algorithms, kernel k-means has gained popularity due to its simple iterative nature and ease of implementation. However, its run-time complexity and memory footprint increase quadratically in terms of the size of the data set, and hence, large data sets cannot be clustered efficiently. In this paper, we propose an approximation scheme based on randomization, called the Approximate Kernel k-means. We approximate the cluster centers using the kernel similarity between a few sampled points and all the points in the data set. We show that the proposed method achieves better clustering performance than the traditional low rank kernel approximation based clustering schemes. We also demonstrate that its running time and memory requirements are significantly lower than those of kernel k-means, with only a small reduction in the clustering quality ...
In this paper, a new paradigm of clustering is proposed, which is based on a new Binarization of Consensus Partition Matrix (Bi-CoPaM) technique. This method exploits the results of multiple clustering experiments over the same dataset to... more
In this paper, a new paradigm of clustering is proposed, which is based on a new Binarization of Consensus Partition Matrix (Bi-CoPaM) technique. This method exploits the results of multiple clustering experiments over the same dataset to generate one fuzzy consensus partition. The proposed tunable techniques to binarize this partition reflect the biological reality in that it allows some genes to be assigned to multiple clusters and others not to be assigned at all. The proposed method has the ability to show the relative ...
