本論文針對在動態的P2P網路上，分散式大項目集探勘的問題作研究。本論文利用非推導式大項目集(Non-derivable Itemset Mining : NDI)的方法，在P2P網路上做大項目集的探勘，並且當網路拓撲發生改變(動態事件)時如：有節點加入或離開，需要將之前收集到的資訊做一些修改，本論文提出了一個處理方法，使得當有節點加入或離開時，不用整個網路放棄原本已經探勘過的資訊，我們可以直接做一些修改處理，不用全部重算，如果全部重新探勘，是很耗時又費力的，也浪費了動態事件發生前的資訊。

We propose a number of distributed frequent itemset mining algorithms by extending previous efficient parallel algorithms that exploit the downward closure property. We show that the approach that exploits downward closure property is much more efficient, and more scalable in a distributed setting than approaches.

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