The paper studies the problem of incremental pattern mining from semi-structrued data. When a new dataset is added into the original dataset, it is difficult for existing pattern mining algorithms to incrementally upd...The paper studies the problem of incremental pattern mining from semi-structrued data. When a new dataset is added into the original dataset, it is difficult for existing pattern mining algorithms to incrementally update the mined results. To solve the problem, an incremental pattern mining algorithm based on the rightmost expansion technique is proposed here to improve the mining performance by utilizing the original mining results and information obtained in the previous mining process. To improve the efficiency, the algorithm adopts a pruning technique by using the frequent pattern expansion forest obtained in mining processes. Comparative experiments with different volume of initial datasets, incremental datasets and different minimum support thresholds demonstrate that the algorithm has a great improvement in the efficiency compared with that of non-incremental pattern mining algorithm.展开更多
文摘The paper studies the problem of incremental pattern mining from semi-structrued data. When a new dataset is added into the original dataset, it is difficult for existing pattern mining algorithms to incrementally update the mined results. To solve the problem, an incremental pattern mining algorithm based on the rightmost expansion technique is proposed here to improve the mining performance by utilizing the original mining results and information obtained in the previous mining process. To improve the efficiency, the algorithm adopts a pruning technique by using the frequent pattern expansion forest obtained in mining processes. Comparative experiments with different volume of initial datasets, incremental datasets and different minimum support thresholds demonstrate that the algorithm has a great improvement in the efficiency compared with that of non-incremental pattern mining algorithm.
