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基於異質限制條件的頻繁型樣探勘方法 = Mining Frequent ...

國立高雄大學電機工程學系碩士班

基於異質限制條件的頻繁型樣探勘方法 = Mining Frequent Patterns with Heterogeneous Constraints

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	Mining Frequent Patterns with Heterogeneous Constraints
作者:	黃科瑋,
其他團體作者:	國立高雄大學
出版地:	[高雄市]
出版者:	撰者;
出版年:	2008[民97]
面頁冊數:	76面圖，表 : 30公分;
標題:	基於限制關聯規則探勘
標題:	Constraint-based association mining
電子資源:	http://handle.ncl.edu.tw/11296/ndltd/81649642081809966976
附註:	指導教授：林文揚
附註:	參考書目：面61-64
摘要註:	近年來，基於限制條件的關聯法則探勘的議題在資料探勘研究領域逐漸受到注目。憑藉著允許比傳統方法更多的使用者定義限制，像是最小支持度還有最小信賴度, 在這主題上的研究工作致力於真實呈現分析者的興趣，並減輕其從過多的規則中找出真正使用者有興趣的規則，最終目的在於實現可進行關聯分析的互動環境。然而到目前為止，大部份基於限制頻繁項目集探勘的研究通常都是以單一限制為主，即只考慮到單一類型的條件限制；有關如何處理在複合型限制上的研究仍極為少見的。本論文便是在研究此複合型條件限制的問題。具體來說，我們考慮了三種條件限制，包括項目限制，聚集函數限制和基數限制。我們提出了二個有效率的演算法，稱為MCApriori和MCFPTree可以發掘出滿足上述三種條件限制的頻繁項目集。實驗結果顯示我們的演算法比最先進的頻繁樣式產生方法，如Apriori及FP-Growth，先產生出頻繁項目集後，再依據使用者定義的條件限制生成出滿足所有條件的頻繁項目集的做法還要快上許多。 Recently, the topic of constraint-based association mining has received increasing attention within the data mining research community. By allowing more user-specified constraints other than traditional rule measurements, e.g., minimum support and confidence, research work on this topic endeavor to reflect real interest of analysts and relief them from the overabundance of rules, and ultimately, fulfill an interactive environment for association analysis. So far most work on constraint-based frequent patterns (itemsets) mining has been single-constraint oriented, i.e., only one specific type of constraint is considered. Surprisingly little research has been conducted to deal with multiple types of constraints. This thesis is an investigation on this problem. Specifically, three different types of constraints are considered, including item constraint, aggregation constraint, and cardinality constraint. We propose two efficient algorithms, MCApriori and MCFPTree, to accomplish the task of discovering frequent patterns (itemsets) that satisfy all three types of constraints. Experimental results show that our algorithms are significantly faster than the intuitive approach, i.e., post processing the frequent patterns generated by leading algorithms, such as Apriori and FP-Growth, against user-specified constraints.

基於異質限制條件的頻繁型樣探勘方法 = Mining Frequent Patterns with Heterogeneous Constraints
黃, 科瑋

基於異質限制條件的頻繁型樣探勘方法 = Mining Frequent Patterns with Heterogeneous Constraints / 黃科瑋撰 - [高雄市] : 撰者, 2008[民97]. - 76面 ; 圖，表 ; 30公分.
指導教授：林文揚參考書目：面61-64.
基於限制關聯規則探勘Constraint-based association mining

基於異質限制條件的頻繁型樣探勘方法 = Mining Frequent Patterns with Heterogeneous Constraints
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