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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/111175

    Title: 基於MapReduce程式架構下的分散式循序樣式探勘方法之研究
    Other Titles: A study of distributed sequential pattern mining method based on MapReduce programming model
    Authors: 陳智翔;Chen, Jhih-Siang
    Contributors: 淡江大學資訊管理學系碩士班
    Keywords: Hadoop;MapReduce;循序樣式;資料探勘;sequential pattern;data mining
    Date: 2016
    Issue Date: 2017-08-24 23:45:53 (UTC+8)
    Abstract: 循序樣式探勘是在巨量循序資料庫中用來取得頻繁循序樣式的一種資料探勘方法,常見的循序資料探勘方法可以分為兩大類,候選樣式產生與樣式成長方法,這些演算法主要執行於單機的環境,便會造成一些缺點,像是對於巨量資料的掃描時間、可擴展性的問題、對於巨量資料及的效率較低。為了增進循序資料探勘的性能,並且改善可擴展性的問題,本研究提出了以Hadoop平台與MapReduce軟體架構為基礎的循序資料探勘方法。
    Sequential pattern mining is a data mining method for obtaining frequent sequential patterns in a large sequential database. Conventional sequence data mining methods could be divided into two categories: Apriori-like methods and pattern growth methods. These algorithms are mainly executed on standalone environment. There are some disadvantages like large database scanning time, scalability problem, less efficient for massive dataset. To improve the performance of sequential pattern mining and to improve the scalability issues, this study presents a distributed sequential pattern mining method based on Hadoop platform and Map Reduce programming model. Mining tasks are decomposed to many distributed tasks, the Map function is used to mine each sequential pattern in a subset of database. Then the Reduce function merges together all these identified patterns. It simplifies the search space and acquires a higher mining efficiency. In this study, we have further discussion on the influence of the setting of user-specified minimum support threshold on the distributed mining process. According to our experiments, it has been found that the threshold setting should be different in Map and Reduce mining process to prevent loss of some frequent patterns.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

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