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研究生:王藝筌
研究生(外文):Yi-Chuan Wang
論文名稱:基於時間間隔特徵樣式的分類方法
論文名稱(外文):A Classification Method Based on Interval Pattern Mining
指導教授:陳以錚
指導教授(外文):Yi-Cheng Chen
口試委員:施國琛惠霖
口試日期:2017-07-19
學位類別:碩士
校院名稱:淡江大學
系所名稱:資訊工程學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:29
中文關鍵詞:資料探勘序列樣式探勘時間間隔樣式探勘關聯法則分類器
外文關鍵詞:Data miningInterval-based MiningSequential Pattern MiningClassificationAssociation Classification
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為了能夠挖掘並且瞭解大量資料中所隱含的資訊,其中是以關聯法則為基礎的方法最為著名且廣泛地運用,而最廣為人知的應用則是藉由找出大量資料間的特殊關係來進行分類以達到預測的目的。大多數研究都著重於利用時間點的資料進行序列探勘的分類法則,然而,在實際應用的例子中,不可忽略事件與事件發生時相互呼應而產生時間的關聯性、順序性,例如:電器的使用時間、患者病症發作時間。事件資料之間具有順序性的序列資料,因其大量出現於生活中,於是我們相當注重這一議題。在本篇研究當中,我們利用時間端點表示法來表示事件與事件之間的關係,採用P-TPMiner (Probabilistic Temporal Pattern Miner)來找尋所有頻繁之時間序列,整合樣式探勘與分類方法之模型,藉此制訂分類規則的計算機制,進行預測序列資料所屬之類別。從實驗結果可得知,此序列資料分類方法不只有效率且可擴性高,並且預測結果具有可靠的正確率。
Most classification methods on sequential pattern mining are revolved about time point-based event data. Few researches utilize discovered temporal pattern for classifying. However, in many real world applications, there are relationships between events. In this paper, these relationships are simulated using a coincidence representation that extends Allen’s interval algebra. Moreover, we employ an efficient pattern mining algorithm called P-TPMiner (Probabilistic Temporal Pattern Miner) is designed to discover frequent time-interval based patterns. Exploiting the discovered temporal patterns, we proposed a classification method which is based on interval pattern. Experiments result is not only efficient and scalable, but also the accuracy is great on both synthetic and real datasets.
TABLE OF CONTENT
CHINESE ABSTRACT III
ABSTRACT IV
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 RELATED WORK 5
CHAPTER 3 PRELIMINARIES 9
CHAPTER 4 PROBABILISTIC TEMPORAL PATTERN MINER 13
CHAPTER 5 METHOD 16
CHAPTER 6 EXPERIMENTAL RESULTS 23
CHAPTER 7 CONCLUSION 26
REFERENCES 27



TABLE OF FIGURE
FIGURE 1: ILLUSTRATION OF TEMPORAL INTERVALS …………………...2
FIGURE 2: ARCHITECTURE OF THE INTEGRATED PROCESSES ………….3
FIGURE 3: EXAMPLE EVENT SEQUENCE WITH FOUR EVENTS ………….6
FIGURE 4: P-TPMINER ALGORITHM TO DISCOVER THE OPP …………....13
FIGURE 5: FUNCTION P-TPSPAN …………………………………………....15
FIGURE 6: THE STRUCTURE OF THE CLASSIFICATION PROCEDURES ...16
FIGURE 7: ALGORITHM TO ASSIGN CLASS LABEL BASED ON MAJORITY_VOTE …………………………………………………………...18
FIGURE 8: ALGORITHM TO ASSIGN CLASS LABEL BASED ON BEST_SELECTION …………………………………………………………...19
FIGURE 9: ALGORITHM TO ASSIGN CLASS LABEL BASED ON HYBRID 21
FIGURE 10: EXAMPLES OF GRADING THE RAW DATA ………………….24



TABLE OF TABLE
TABLE 1: ENDPOINT AND ENDTIME REPRESENTATIONS OF ALLEN’S 13 TEMPORAL RELATIONS ……………………………………………………10
TABLE 2: THE CLASSIFYING CONDITION EXAMPLES OF ONE UNKNOWN SEQUENCE ……………………………………………………..20
TABLE 3: EXAMPLES OF THE DATABASE WHICH IS REPRESENTED BY ENDPOINT REPRESENTATION ……………………………………………..24
TABLE 4: TESTING ACCURACY ……………………………………………24
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