Abstract:

Traditional information mining technology has been unable to meet the increasingly complex application requirements in the big data environment. The distributed data mining technique is a means to solve this problem. An improved distributed associative classification algorithm based on improved FP-tree was presented. First, FP-Tree was optimized in each local node to generate local conditional pattern tree (CFP-Tree), and then a global CFP-Tree was constructed through the inter-site transmission of each CFP-Tree. Second, the initial global significant classification rules were obtained by calculating significant degree in the process of global CFP-Tree mining. Final, the pruning strategies were used to get a small set of rules to construct the overall associative classifier. Experimental results show that this algorithm can not only effectively reduce network traffic and improve mining efficiency, but also ensure ensuring statistical significance of rules and improve the ability for the discovery of implicit rules.

Key words: associative classification, distributed information mining, FP-tree, conditional pattern tree, significant degree