The K-means algorithm is among the most commonly used data clustering methods. However, regular can only be applied in input space, and it applicable when clusters are linearly separable. kernel K-means, which extends into able to capture nonlinear structures identify arbitrarily shaped clusters. methods often operate on matrix of data, scale poorly with size matrix, or suffer from high cost du...

In this paper, we consider the scheduling problem of minimizing total weighted completion time on uniform machines with unbounded batch. Each of the machine Ml (l = 1, ⋅ ⋅ ⋅ ,m) has a speed sl and can process up to B(≥ n) jobs simultaneously as a batch. The processing time of a batch denoted by P(B) is given by the processing time of the longest job in it, and the running time of the batch on m...

The use of batch means is a well known technique for estimating the variance of point estimators computed from simulation experiments. The batch means variance estimator is simply the (appropriately scaled) sample variance of the estimator computed on subsets of consecutive observations. For the method to be practical, a good choice of batch length is necessary. We propose a method to estimate ...

In this paper, we firstly describe a technique named Duplicating, which duplicates machines in batch scheduling environment. Then we discuss several applications of Duplicating in solving batch scheduling problems, for the batch scheduling problem on unrelated parallel machines to minimize makespan, we give an (4− 2 B )-approximation algorithm and an (2− 1 B + ǫ) algorithm when m is fixed. We a...