挖掘頻繁項(xiàng)集是數(shù)據(jù)挖掘應(yīng)用中關(guān)鍵的問題。經(jīng)典的FP-growth 算法利用FP-tree 有效的壓縮了數(shù)據(jù)集的規(guī)模，但是在挖掘過程中需要反復(fù)遞歸構(gòu)造條件FP-tree 成為限制算法
效率的瓶頸。本文通過將FP-tree 映射成矩陣，通過在矩陣自身上進(jìn)行偽投影得到條件模式陣，避免了遞歸構(gòu)造FP-tree，從而節(jié)約了內(nèi)存消耗和計(jì)算時(shí)間。
關(guān)鍵詞：數(shù)據(jù)挖掘；關(guān)聯(lián)規(guī)則；頻繁項(xiàng)集；矩陣
Abstract: It is key point of data mining application mining frequent itemsets. Classic frequent
itemsets mining algorithm FP-growth compresses the scale of dataset effectively using FP-tree
structure. But it has own bottleneck that for getting complete frequent itemsets it need build
conditional FP-tree recursively in the mining process. This paper proposes a new frequent itemsets mining algorithm that maps FP-tree structure into FP-array and mines upon it. In the mining process, this algorithm can avoid building conditional FP-tree. So, it saves time and memory very much.
Key words: data mining; association rule; frequent itemsets; array