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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/74732

    Title: 植基於Spark系統之分散式粒化運算決策產生演算法;A Distributed Decision Generation Algorithm based on Granular Computing Using Spark
    Authors: 林子晏;Lin, Zi-Yan
    Contributors: 資訊工程學系
    Keywords: 分類演算法;分散式粒化運算決策產生演算法;Hadoop;Spark;DGAGC;Classification
    Date: 2017-08-16
    Issue Date: 2017-10-27 14:37:45 (UTC+8)
    Publisher: 國立中央大學
    Abstract: Classification演算法的特色是分成兩個階段,第一個階段是training,用已經分類的資料並根據資料的特徵做出對應的類別,第二個階段是Classification,對其他未經分類資料的特徵做分類。DGAGC是一種Classification演算法,適用於離散型資料,連續型資料需要額外處理。我們過去的研究已經讓DGAGC支援Hadoop MapReduce運算模型。但是Hadoop MapReduce的版本只針對DGAGC training的部分。在Classification部分,只有單機版本。其中以training的部分最花時間。本篇論文提出了Spark版本的DGAGC training與Classification,藉此來改善Hadoop版本在資料集運算量不算大時的執行效率。再來是DGAGC Classification的部分,單機版本在預測模型太大的時候就無法進去預測。所以提出Spark版本的DGAGC Classification改善此問題。;The DGAGC algorithm, developed by National Central University, is a classification algorithm based on association-rule mining and searching. The DGAGC algorithm also specifies a distributed computing approach for model training, which is implemented on top of Hadoop MapReduce. In this study, we propose a new distributed computing approach for the DGAGC algorithm based on Apache Spark. With the support of in-memory computing by Spark, the new distributed DGAGC algorithm can achieve less average execution time for model training, given four different training data sets. In addition, we also propose a distributed version of the DGAGC for data classification.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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