Top-Down Mining Frequent Closed Patterns in Microarray Data

Mining frequent closed patterns play an important role in mining association rules in microarray data. The bottom-up search strategy for mining frequent closed patterns cannot make full use of minimum support threshold to prune search space and results in long runtime and much memory overhead. TP+close algorithm based on top-down search strategy addressed the problem. However, it determined a frequent pattern was closed by scanning the set of frequent closed patterns that have been found. For dense datasets, the algorithm performance will be seriously affected by the scan time. In this paper, we proposed an improved tree structure, TTP+tree. Based on the tree, a top-down algorithm, TTP+close, was developed for mining frequent closed patterns in microarray data. TTP+close checked the closeness property of itemset by the trace-based method and thus avoided scanning the set of frequent closed patterns. The experiments show that TTP+close outperforms TP+close when dealing with dense data.