FP-Tree不同實作方式之效能比較; FP-Tree in different implement methods to compare the performances

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/12844

Title:	FP-Tree不同實作方式之效能比較;FP-Tree in different implement methods to compare the performances
Authors:	趙書榮;Shu-Jun Chao
Contributors:	資訊管理研究所
Keywords:	資料挖掘;關聯規則;演算法;FP-tree;data mining;association rule;algorithm;FP-tre
Date:	2002-10-18
Issue Date:	2009-09-22 15:15:51 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	摘要目前挖掘關聯規則的演算法可依需不需產生candidate itemset的作法分為兩類，例如Frequent-Pattern tree與Apriroi-like approach。此兩者最主要的差異在於，FP-tree並不產生candidate itemsets，它將資料庫壓縮在Frequent-Pattern tree的結構中，避免多次的高成本的資料庫掃瞄；後者是需要產生candidate itemset的方法。而本文的目的是以應用Frequent-Pattern tree之理論，在實作方面以不同資料結構技術作效能比較測試，得到以那一種資料結構應用在Frequent-Pattern tree上執行時間之效能較佳。在本文中共建立了(一)FP-tree_tail演算法，tail為在head table中增加一個tail欄位，(二)FP-tree_hash演算法，hash為以hash function計算出每個node所在位置方式建立FP-tree，(三)FP-tree_hash+tail演算法，為結合(一)、(二)之優點，所完成之演算法.，並將以上三個演算法與傳統FP-tree演算法一起比較，以找出各演算法之優缺點。經由本文實驗測試資料數據中，發現在各種實驗參數下，傳統FP-tree演算法所需花費之時間，為三個改良FP-tree演算法的數十倍。 now the algorithm in the association rules can be seperated two kinds.first is Apriori-like approach.second is Frequent-Pattern tree.main different between the above is the Frequent-Pattern tree did not to generate the candidate itemsets.its avoid a huge cost to scan database many times. this paper apply three different data structure(FP-tree_tail,FP-tree_hash,FP-tree_hash+tail) to improve Frequent-Pattern tree algorithm .then to compare the performance about them , accroding to the test data we found the performance of the FP-tree alogrithm are worst then the other algorithms many times.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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