A Study of Improving the Performance of Mining Multi-Valued and Multi-Labeled Data

Abstract

Nowadays data mining algorithms are successfully applying to analyze the real data in our life to provide useful suggestion. Since some available real data is multi-valued and multi-labeled, researchers have focused their attention on developing approaches to mine multi-valued and multilabeled data in recent years. Unfortunately, there are no algorithms can discretize multi-valued and multi-labeled data to improve the performance of data mining. In this paper, we proposed a novel approach to solve this problem. Our approach is based on a statistical-based discretization metric and the simulated annealing search algorithm. Experimental results show that our approach can effectively improve the performance of the-state-of-art multi-valued and multi-labeled classification algorithm.