No abstract

In recent years, document clustering has been receiving more and more attentions as an important and fundamental technique for unsupervised document organization, automatic topic extraction, and fast information retrieval or filtering. In this paper, we propose a novel method for clustering documents using regularization. Unlike traditional globally regularized clustering methods, our method first construct a local regularized linear label predictor for each document vector, and then combine all those local regularizers with a global smoothness regularizer. So we call our algorithm Clustering with Local and Global Regularization (CLGR). We will show that the cluster memberships of the documents can be achieved by eigenvalue decomposition of a sparse symmetric matrix, which can be efficiently solved by iterative methods. Finally our experimental evaluations on several datasets are presented to show the superiorities of CLGR over traditional document clustering methods.

No abstract

Swap trailer transport organisation problem originates from the traditional vehicle routing problem (VRP). Most of the studies on the problems assume that the travelling times of vehicles are fixed values. In this paper, the uncertainties of driving times are considered and a chance constrained programming problem is proposed. An improved simulated annealing algorithm is used to solve the problem proposed. The model and algorithm described in this paper are studied through a case study, and the influence of uncertainty on the results is analysed. The conclusion of this study provides theoretical support for the practice of trailer pickup transport.