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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/102332

    Title: 可處理巨量資料的平行化CHAID決策樹
    Other Titles: Paralleled CHAID decision tree algorithm with big-data capability
    Authors: 蔡育儒;Tsai, Yu-Ju
    Contributors: 淡江大學統計學系碩士班
    Keywords: 資料探勘;分類器;CHAID決策樹;平行化;data mining;classifiers;parallel;CHAID
    Date: 2014
    Issue Date: 2015-05-04 09:53:09 (UTC+8)
    Abstract: 隨著科技的進步,Big-Data的時代正式來臨。在資料量急增下,電腦處理速度的改良已成為一項重要的發展技術。若將資料處理及分析的時間縮短,可以提早進行預測或判斷,平行化處理就是減少分析時間的一個方法。本研究探討資料探勘常被使用的決策樹方法與平行化運算的結合。我們改寫了CHAID決策樹在合併及判斷變數的運算法則,利用多核心計算,使決策樹的建構時間縮短。在結論中,模擬的結果顯示,當CPU 的核心為一顆以上時,CHAID決策樹的計算時間比單核心狀況明顯縮短。在處理更大的資料量時,我們節省的時間會有更明顯的差異。
    As technology advances, the era of Big-Data has finally arrived. As the amount of data increases , the improvement of computing speed becomes an important development technology. If data training and analysis time are reduced, we could make the prediction or decision much earlier then expected. As a result, parallel computation is one of the methods which can reduce the analysis time. In this paper, we rewrite the CHAID decision tree algorithm for parallel computation and Big-Data capability. Our simulation results show that, when the CPU has more than one kernel, the computation time of our improved CHAID tree is significantly reduced. When we have a huge amount of data, the difference of computation times is even more significant.
    Appears in Collections:[統計學系暨研究所] 學位論文

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