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系統識別號 U0002-2306200818482900
中文論文名稱 負相序列型樣與模糊相關規則之探勘方法
英文論文名稱 Algorithms for Negative Sequential Pattern Mining and Fuzzy Correlation Rule Mining
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 96
學期 2
出版年 97
研究生中文姓名 陳宏任
研究生英文姓名 Hung-Jen Chen
學號 889190020
學位類別 博士
語文別 英文
口試日期 2008-06-04
論文頁數 83頁
口試委員 指導教授-林丕靜
委員-陳穆臻
委員-蔣定安
委員-王亦凡
委員-葛煥昭
委員-林丕靜
中文關鍵字 資料探勘  負相序列型樣  相關規則  頻繁型樣 
英文關鍵字 Data Mining  Negative Sequential Pattern  Correlation Rule  Frequent Pattern 
學科別分類 學科別應用科學資訊工程
中文摘要 隨著資訊科技與資料自動收集設備的快速發展,已有大量的資料被收集並儲存在各式各樣的儲存設備上。從這些大量的資料中萃取出有價值的資訊加以運用,已成為企業組織提升競爭力的重要關鍵。資料探勘提供了在大量資料庫中挖掘出重要的、未知的以及可用的資訊的一些自動化方法。而頻繁型樣探勘在資料探勘領域中一直是個相當重要的研究議題。
已有許多方法被提出來探勘各式各樣的頻繁型樣。這些型樣包括:頻繁項目集、關聯規則、序列型樣和相關規則等。本論文提出三個演算法分別探勘三種新型的頻繁型樣:負相序列型樣、負相模糊序列型樣和模糊相關規則。
我們所提的負相序列型樣探勘演算法,有別於傳統的序列型樣演算法,不僅考慮到交易紀錄中有出現的項目,同時也把沒有出現在交易紀錄上的項目考慮進來。在探勘過程中,我們運用先驗法則來減少多餘候選序列的產生。並且,我們定義了一個以條件機率為基礎的蘊義衡量函數(Interestingness measure),藉以找出涵義較高的負相序列型樣。
因為大多數實際的交易資料都是數量型資料,為了分析資料庫中具有數量值的項目,我們結合模糊集合論與前述負相序列型樣探勘演算法,提出另一演算法:負相模糊型樣探勘演算法。該演算法將數量型項目模糊化為模糊項目,從而在數量型資料庫中找出負相模糊序列型樣。
此外,我們也針對模糊相關規則的探勘提出演算法。該演算法藉由模糊相關分析來判斷兩個模糊項目集在資料庫中的相關性,從而在數量型資料庫中探勘出模糊相關規則。
實驗顯示,我們所提的三個演算法能夠有效避免產生大量多餘的候選項目集和候選序列,並且能夠找出數量較精簡、涵意較高的頻繁型樣。

英文摘要 Due to rapid developments in information technology and automatic data collection tools, a large amount of data has been collected and stored in various data repositories. To extract valuable information from these data is the key to improve business competition. Data mining offers ways to automatically find nontrivial, previously unknown, and potentially useful knowledge from large databases. Mining of frequent patterns plays an essential role in data mining.
Many methods have been proposed for discovering various types of frequent patterns such as frequent itemsets, association rules, correlation rules, and sequential patterns. In this dissertation, three types of frequent patterns, namely, negative sequential patterns, negative fuzzy sequential patterns, and fuzzy correlation rules, have been introduced.
We propose an algorithm for mining negative sequential patterns, which consider not only the occurrence of itemsets in transactions in databases but also their absence. In this algorithm, we have designed a candidate generation procedure employing the apriori principle to eliminate many redundant candidates during the mining task. Moreover, in this method, we also define a function based on the conditional probability theory to measure the interestingness of sequences in order to find more interesting negative sequential patterns.
Additionally, most transaction data in real-world applications usually consist of quantitative values. In order to investigate various types of data in quantitative databases and then discover negative sequential patterns from such databases, we propose an algorithm, which combines fuzzy-set theory and negative sequential pattern concept, for mining negative fuzzy sequential patterns from quantitative databases.
Furthermore, we propose a method for mining fuzzy correlation rules, which applies fuzzy correlation analysis to determine whether two sub-fuzzy itemsets in a fuzzy itemset are dependent, and then extract more interesting fuzzy correlation rules from quantitative databases.
Experiments in the three proposed algorithms show that our algorithms can prune a lot of redundant candidates during the process of mining tasks and can effectively extract frequent patterns that are actually interesting.
論文目次 Table of Contents-----------------------------------------------------------------I
List of Figures---------------------------------------------------------------------IV
List of Tables-----------------------------------------------------------------------V
Chapter 1 Introduction-----------------------------------------------------------1
1.1 Motivation--------------------------------------------------------------------1
1.2 Research Objective----------------------------------------------------------3
1.3 Organization of this Dissertation------------------------------------------4
Chapter 2 Background Knowledge---------------------------------------------5
2.1 Data Mining------------------------------------------------------------------5
2.1.1 Classification---------------------------------------------------------7
2.1.2 Clustering Analysis--------------------------------------------------7
2.1.3 Outlier Analysis------------------------------------------------------8
2.1.4 Evolution Analysis---------------------------------------------------8
2.2 Frequent Pattern Mining----------------------------------------------------9
2.2.1 Association Rules and Correlation Rules------------------------9
2.2.2 Sequential Patterns-------------------------------------------------15
Chapter 3 Mining Negative Sequential Patterns---------------------------16
3.1 Preliminary------------------------------------------------------------------16
3.2 Problem Statement---------------------------------------------------------16
3.2.1 Sequential Patterns------------------------------------------------16
3.2.2 Negative Sequential Patterns------------------------------------18
3.3 Related Work---------------------------------------------------------------20
3.4 Algorithm NSPM-----------------------------------------------------------21
3.4.1 Procedure NSP-----------------------------------------------------21
3.4.2 Candidates Generation-------------------------------------------22
3.5 Example Experiment-------------------------------------------------------29
3.6 Summary---------------------------------------------------------------------35
Chapter 4 Mining Negative Fuzzy Sequential Patterns------------------36
4.1 Preliminary------------------------------------------------------------------36
4.2 Problem Statement---------------------------------------------------------36
4.2.1 Sequential Patterns--------------------------------------------------36
4.2.2 Negative Sequential Patterns---------------------------------------38
4.2.3 Fuzzy Sequential Patterns------------------------------------------39
4.2.4 Negative Fuzzy Sequential Patterns-------------------------------40
4.3 Related Work---------------------------------------------------------------41
4.4 Algorithm NFSPM---------------------------------------------------------43
4.4.1 Procedure NFSP------------------------------------------------------44
4.4.2 Candidates Generation-----------------------------------------------45
4.5 Example Experiment-------------------------------------------------------51
4.6 Summary--------------------------------------------------------------------58
Chapter 5 Mining Fuzzy Correlation Rules---------------------------------59
5.1 Preliminary------------------------------------------------------------------59
5.2 Problem Statement---------------------------------------------------------60
5.3 Fuzzy Correlation Analysis-----------------------------------------------63
5.4 The Proposed Method-----------------------------------------------------65
5.5 Example Experiment-------------------------------------------------------67
5.6 Summary--------------------------------------------------------------------71
Chapter 6 Conclusion and Future Work------------------------------------73
6.1 Conclusion------------------------------------------------------------------73
6.2 Future Work-----------------------------------------------------------------74
References--------------------------------------------------------------------------75


List of Figures

Fig. 2.1 The process of knowledge discovery in databases--------------------6
Fig. 2.2 The apriori algorithm--------------------------------------------------11
Fig. 2.3 Procedure apriori_gen--------------------------------------------------12
Fig. 2.4 An example of the process of the Apriori algorithm---------------13
Fig. 3.1 Algorithm NSPM---------------------------------------------------------25
Fig. 3.2 Procedure NSP-----------------------------------------------------------26
Fig. 3.3 Procedure p_gen---------------------------------------------------------27
Fig. 3.4 Procedure n_ge-----------------------------------------------------------28
Fig. 4.1 Algorithm NFSPM-------------------------------------------------------47
Fig. 4.2 Procedure NFSP----------------------------------------------------------48
Fig. 4.3 Procedure cp_gen-------------------------------------------------------49
Fig. 4.4 Procedure cn_gen--------------------------------------------------------50
Fig. 4.5 The fuzzy membership functions for the fuzzy sets of items------52

List of Tables

Table 2.1 An example transaction database D---------------------------------12
Table 2.2 All the association rule candidates generated from itemset {B, C, E}, and their confidence-------------------------------------------14
Table 2.3 All association rules found from the example database ----------14
Table 3.1 The notations used in algorithm NSPM and the related procedures-----------------------------------------------------------24
Table 3.2 A transaction dataset--------------------------------------------------30
Table 3.3 A customer sequence database transformed from Table 3.2-----30
Table 3.4 Large items-------------------------------------------------------------31
Table 3.5 Recorded large items--------------------------------------------------31
Table 3.6 1-Sequences------------------------------------------------------------31
Table 3.7 Positive 2-sequence----------------------------------------------------32
Table 3.8 Negative 2-sequences-------------------------------------------------32
Table 3.9 Positive 3-sequences---------------------------------------------------33
Table 3.10 Negative 3-sequences------------------------------------------------34
Table 3.11 Obtianed negative sequential patterns after calling the NSP procedure-------------------------------------------------------------34
Table 3.12 Obtained interesting negative sequential patterns----------------35
Table 4.1 The notations used in algorithm NFSPM and the related procedures-----------------------------------------------------------46
Table 4.2 A quantitative transaction dataset------------------------------------51
Table 4.3 Fuzzy sets transformed from Table 4.2-----------------------------52
Table 4.4 Large 1-Fuzzy items---------------------------------------------------53
Table 4.5 Large 2-Fuzzy items---------------------------------------------------53
Table 4.6 Mapped large fuzzy itemsets-----------------------------------------53
Table 4.7 1-sequences-------------------------------------------------------------54
Table 4.8 Positive 2-sequences---------------------------------------------------54
Table 4.9 Negative 2-sequences-------------------------------------------------55
Table 4.10 Positive 3-sequences-------------------------------------------------56
Table 4.11 Negative 3-sequence-------------------------------------------------57
Table 4.12 Obtained interesting negative sequential patterns----------------57
Table 5.1 A random sample with 12 fuzzy records---------------------------68
Table 5.2 The fuzzy supports of fuzzy items in F-----------------------------68
Table 5.3 The fuzzy support, fuzzy correlation coefficient and t valueof testing the fuzzy correlation coefficient of each element of C2------------------------------------------------------------------------69
Table 5.4 The fuzzy support, fuzzy correlation coefficient and t valueof testing the fuzzy correlation coefficient of each element of C3------------------------------------------------------------------------70
Table 5.5 The fuzzy confidences of candidate fuzzy correlation rules-----70
Table 5.6 Obtained fuzzy correlation rules-------------------------------------71
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