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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/6813

    Title: 診斷資料粒子化之相容程度-SQL於資料挖掘之應用
    Data mining: a SQL-based approach to tuple consistency recognition for granulized datasets
    Authors: 高淑珍
    Keywords: 資料探勘
    Data mining
    Date: 2002-07-31
    Issue Date: 2009-12-31 15:12:39 (UTC+8)
    Abstract: 許多探討連續性資料之間斷化技術的研究,其主要目的是為了提高資料探勘技術的效能。然而,在間斷化的過程中,不相容資料的比率卻並未被加以探討,導致所得到的結論並不是非常可靠。本研究專題以此為動機,探討連續性資料間斷化後之資料不相容程度,藉此鼓勵相關研究在研究過程中能考慮這個問題,以提高研究結論的可靠度。本研究搜集了十個實務性資料庫做為測試之用。間斷法係採等距法,每個區間以三十筆為基礎,最多不超過七個區間。實驗結果發現:有50%的資料庫含有不相容的資料;其中有30%的資料庫,其不相容程度超過15%。
    In this study, the problem of inconsistent tuples that exist in the granulized datasets was considered. It is believed that too high degree of tuple inconsistency will definitely decrease the reliability of conclusion in the data mining related research. An empirical investigation where 10 real life continuous datasets were used was conducted to confirm the study concern. The technique of equal width interval was employed to transform the continuous data. The remarkable results showed that 50% of the used datasets contained inconsistent tuples; 30% contained more than 15% inconsistent tuples.
    Appears in Collections:[資訊管理系所] 研究計畫

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