The classic K-Nearest Neighbor (KNN) classification algorithm is widely used in text classification. This paper proposes an efficient algorithm for text classification by improving the traditional TF-IDF based KNN text classification algorithm. In addition, the MapReduce parallel implementation of the new algorithm on the Hadoop platform is introduced to improve the capacity of the KNN algorithm to process large data sets. The experimental results show that the new algorithm implemented through MapReduce not only improves the classification accuracy, but also has the advantages of fast convergence and good scalability.

In this paper, we obtain a new equivalent fixed-point form of the linear complementarity problem by introducing a relaxed matrix and establish a class of relaxed modulus-based matrix splitting iteration methods for solving the linear complementarity problem. Some sufficient conditions for guaranteeing the convergence of relaxed modulus-based matrix splitting iteration methods are presented. Numerical examples are offered to show the efficacy of the proposed methods.