由於網際網路的興起，到處都可以看到大量的電子文件。因為文件分類可以使人們輕鬆地處理文件，因此最近幾年吸引許多學者對文件分類的注意與研究。
在資料探勘領域中，關聯法則是其中一個重要的研究方向，大部分的關聯法則研究都是著重在尋找正向關聯的關聯規則，然而，許多研究指出，負向關聯的關聯式規則與正向關聯的關聯式規則是一樣重要的。
因此，本研究著重在於利用有趣度量來區分出正負關聯的關聯規則，然而，僅以有趣度量來區分是不夠。在文獻中有些研究利用相關係數大小來判斷規則的強度，而相關係數僅考慮到詞彙間的共同出現或不出現的次數，並無考慮到詞彙的權重，而詞彙在類別中出現頻繁與否是有一定的重要程度，因此，想再將有趣度量結合詞彙之權重來加強正負關聯的關聯規則強度，以正負關聯強度來篩選出所要的關聯規則，使得這些關聯規則是更有意義、更具代表性的代表各類別的分類準則，以提升文件分類的效益，更精準地正確分類文件。

Due to the popularity of World Wide Web, there exist a large amount of digital documensts on the Internet. Because text categorization can make it more easily to deal with these documensts, it attracts many researchers to study the text categorization problem.
In data mining, exploration of association rules is an important research issue. Most association rule researches focus on finding positive association rules. However, many studies point out that negative association rules are as important as positive association rules.
Therefore, in this thesis, we will find out both positive and negative association rules. Although interest measure is a commonly-used measure for text categorization, we find that it is not enough to use interest measure only for text categorization. Some researches use correlation coefficient to judge the strength of a rule, but correlation coefficient only considers absence or presence between terms, not the weight of terms. Besides, it is important to consider term frequencies in categorization. Hence, we would like to combine interest and term weight to enhance the discriminative power of positive and negative association rules. It will be used to filter association rules to make these rules more meaningful and more representative for the classification criterion of a category. Therefore, the categorization results can be improved and new documents can be classified correctly.

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