博碩士論文 etd-0129108-164840 詳細資訊


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姓名 李婉瑞(Wan-Jui Lee) 電子郵件信箱 wrlee@water.ee.nsysu.edu.tw
畢業系所 電機工程學系研究所(Electrical Engineering)
畢業學位 博士(Ph.D.) 畢業時期 96學年第1學期
論文名稱(中) 模糊時間性與週期性關聯法則的探勘
論文名稱(英) Discovery of fuzzy temporal and periodic association rules
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    摘要(中) 在本論文中,我們主要提出了兩種新形式的時間性知識,也就是模糊時間性關聯法則和模糊週期性關聯法則,我們並提出了針對這兩種新知識的探勘方法。
    在我們日常生活中所使用的時間觀念通常都具有不確定性,沒有很嚴格的定義,因此我們發展了模糊曆法代數,以便使用者能夠簡單有效地描述有興趣的時間。同時,經由模糊曆法代數,我們能夠找出所需的時間單元,並從中探勘出模糊時間性和週期性關聯法則。為了能漸進地探勘出模糊時間性關聯法則,我們發展出一個以border為基礎的探勘方法。藉由將有用的資訊儲存在border之中,我們能夠更有效地找出candidate itemset。這些資訊也能更進一步地節省不必要的計算和資料庫讀取的次數。為了找出模糊週期性關聯法則,我們發展了能找出具有週期的pattern的技術。具有週期的pattern會有規律地出現在時間單元上,因此我們除了找尋pattern外,也需找尋週期,而這個問題的困難之處,在於如何找出這些週期。在實際應用所蒐集到的資料庫中所出現的週期,通常都不會太精準,而且時間單位可能具有多重架構。為了找出帶有非同步週期,也就是模糊週期的pattern,我們利用同步週期pattern的資訊以求得一個lower bound,然後利用這個lower bound來產生模糊週期pattern的可能候選。實驗結果證實我們的方法可以有效地找出模糊時間性和模糊週期性關聯法則。
    摘要(英) With the rapidly growing volumes of data from various sources, new tools and computational theories are required to extract useful information (knowledge) from large databases. Data mining techniques such as association rules have been proved to be effective in searching hidden knowledge in a large database. However, if we want to extract knowledge from data with temporal components, it becomes necessary to incorporate temporal semantics with the traditional data mining techniques. As mining techniques evolves, mathematical techniques become more involved to help improve the quality and diversity of mining. Fuzzy theory is one that has been adopted for this purpose. Up to now, many approaches have been proposed to discover temporal association rules or fuzzy association rules, respectively. However, no work is contributed on mining fuzzy temporal patterns.
    We propose in this thesis two data mining systems for discovering fuzzy temporal association rules and fuzzy periodic association rules, respectively. The mined patterns are expressed in fuzzy temporal and periodic association rules which satisfy the temporal requirements specified by the user. Temporal requirements specified by human beings tend to be ill-defined or uncertain. To deal with this kind of uncertainty, a fuzzy calendar algebra is developed to allow users to describe desired temporal requirements in fuzzy calendars easily and naturally. Moreover, the fuzzy calendar algebra helps the construction of desired time intervals in which interesting patterns are discovered and presented in terms of fuzzy temporal and periodic association rules.
    In our system of mining fuzzy temporal association rules, a border-based mining algorithm is proposed to find association rules incrementally. By keeping useful information of the database in a border, candidate itemsets can be computed in an efficient way. Updating of the discovered knowledge due to addition and deletion of transactions can also be done efficiently. The kept information can be used to help save the work of counting and unnecessary scans over the updated database can be avoided. Simulation results show the effectiveness of the proposed system for mining fuzzy temporal association rules.
    In our mining system for discovering fuzzy periodic association rules, we develop techniques for discovering patterns with periodicity. Patterns with periodicity are those that occur at regular time intervals, and therefore there are two aspects to the problem: finding the pattern, and determining the periodicity. The difficulty of the task lies in the problem of discovering these regular time intervals, i.e., the periodicity. Periodicites in the database are usually not very precise and have disturbances, and might occur at time intervals in multiple time granularities. To discover the patterns with fuzzy periodicity, we utilize the information of crisp periodic patterns to obtain a lower bound for generating candidate itemsets with fuzzy periodicities. Experimental results have shown that our system is effective in discovering fuzzy periodic association rules.
    關鍵字(中)
  • 關聯法則
  • 模糊曆法
  • 模糊週期性
  • 模糊時間性
  • 關鍵字(英)
  • fuzzy temporal pattern
  • association rule
  • fuzzy calendar
  • fuzzy periodic pattern
  • 論文目次 1 Introduction 1
    1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
    1.2 Association Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
    1.2.1 Apriori . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
    1.2.2 Incremental Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
    1.3 Fuzzy Calendars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
    1.3.1 Fuzzy Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
    1.3.2 Fuzzy Temporal Calendars . . . . . . . . . . . . . . . . . . . . . . . . . . 13
    1.3.3 Fuzzy Periodic Calendars . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
    1.3.4 Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
    1.4 Mining Fuzzy Temporal Association Rules . . . . . . . . . . . . . . . . . . . . . . 19
    1.5 Mining Fuzzy Periodic Association Rules . . . . . . . . . . . . . . . . . . . . . . . 20
    1.6 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
    2 Fuzzy Calendars 23
    2.1 Fuzzy Calendar Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
    2.2 Fuzzy Temporal Calendar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
    2.3 Fuzzy Periodic Calendar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
    3 Discovery of Fuzzy Temporal Association Rules 37
    3.1 Our System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
    3.2 Mining Fuzzy Temporal Association Rules . . . . . . . . . . . . . . . . . . . . . . 41
    3.2.1 Single Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
    3.2.2 Incremental Updating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
    4 An Illustration of Mining Fuzzy Temporal Association Rules 51
    4.1 For Single Database D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
    4.2 For Additions and Deletions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
    5 Experimental Results of Mining Fuzzy Temporal Association Rules 61
    5.1 Experiment 1: Calendars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
    5.2 Experiment 2: Synthetic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
    5.3 Experiment 3: KDD CUP 2000 Dataset . . . . . . . . . . . . . . . . . . . . . . . 68
    6 Discovery of Fuzzy Periodic Association Rules 73
    6.1 Our Mining System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
    6.1.1 Association Rules in Time Intervals . . . . . . . . . . . . . . . . . . . . . 74
    6.1.2 Fuzzy Periodic Association Rules . . . . . . . . . . . . . . . . . . . . . . . 75
    6.2 Mining Fuzzy Periodic Association Rules . . . . . . . . . . . . . . . . . . . . . . . 80
    6.2.1 Discovering Crisp Periodic Patterns . . . . . . . . . . . . . . . . . . . . . 81
    6.2.2 Discovering Fuzzy Periodic Patterns . . . . . . . . . . . . . . . . . . . . . 87
    6.2.3 Incremental Mining of Crisp and Fuzzy Periodic Patterns . . . . . . . . . 88
    7 An Illustration of Mining Fuzzy Periodic Association Rules 90
    7.1 For Crisp Periodic Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
    7.2 For Fuzzy Periodic Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
    8 Experimental Results of Mining Fuzzy Periodic Association Rules 100
    8.1 Experiment 1: Synthetic Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 101
    8.1.1 Discovering Crisp Periodic Association Rules . . . . . . . . . . . . . . . . 103
    8.1.2 Discovering Fuzzy Periodic Association Rules . . . . . . . . . . . . . . . . 104
    8.2 Experiment 2: KDD CUP 2000 Dataset . . . . . . . . . . . . . . . . . . . . . . . 107
    8.2.1 Discovering Crisp Periodic Association Rules . . . . . . . . . . . . . . . . 109
    8.2.2 Discovering Fuzzy Periodic Association Rules . . . . . . . . . . . . . . . . 111
    9 Conclusion 114
    Bibliography 117
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