不确定性数据频繁项集挖掘算法

由于不确定性数据大量存在于传感器网络，移动计算，军事，电信等应用领域，传统的频繁项集挖掘算法难以适用到不确定性数据挖掘。为了解决这个问题，本文提出了一种快速有效的算法，该算法基于可能世界模型，只需要扫描一次数据库，且没有建树的过程，通过实验证明，我们提出的算法比UF_Growth算法效率更高。

Algorithms of Frequent Item Sets Mining for Uncertain Data

Uncertain data exists in many situations, such as sensor networks, mobile computing, military, telecommunications and other applications, which makes it difficult to apply traditional algorithms to mining frequent item sets. To deal with these situations, we propose an efficient algorithm based on possible world model with single scan of database. The algorithm works well without any tree construction. Experimental results show that the efficiency of our algorithm is better than UF_Growth.