由於資訊科技的進步以及網際網路之普及，使得生活周遭充斥著大量的資料，
同時也伴隨著資訊過量之問題。如何快速且正確的取得所需之資訊，已經成為一
個重要且棘手之議題。文件探勘之目的為將看似無用之資料轉換成富有價值之知
識，而如何透過文件探勘技術有效的協助使用者獲取感興趣之資訊正是本研究之
動機。自動化文件分群技術為文件探勘中之熱門研究主題，透過分群演算法，將
文件依其主題分群，將相似之文件歸類於同一個群集中，方便使用者瀏覽與蒐集
資訊。然而，傳統之分群技術應用於文件探勘時卻必須克服多項困難，如文件集
之高維度與高資料量，以及分群結果不易閱讀等問題。
為了解決傳統分群法之不足，本研究改良因應文件探勘需求而產生之FIHC
(Frequent Itemset-based Hierarchical Clustering)演算法，FIHC 使用關聯
規則探勘出之高頻項目集做為分群之依據，其想法為屬於同一個主題之文章會共
享許多關鍵字。因此，將高頻項目集視為被共享之關鍵字，而分群之目的即為找
出共享此類關鍵字之文章。本研究進一步將FIHC 演算法結合本體論以解決一義
多詞之問題與挖掘出潛在於文章中之概念，以提升分群之準確率。此外本研究使
用封閉高頻項目集取代FIHC 演算法使用之高頻項目集，進而提升演算法之效率。
經實驗證實，本研究之方法之分群結果較多數分群法更為精確。

Due to the advance of science and technology and the popularity of the Internet, the explosion of information causes the information overload problem. In order to solve these problems, text mining becomes more and more important, and clustering is a hot topic in text mining. However, many document clustering methods are modifications of traditional clustering algorithms that were originally designed for relational database; these algorithms become impractical in real-world document clustering which requires special handling for high dimensionality, high volume, and ease of browsing.
FIHC is a hierarchical clustering method developed for document clustering, the intuition of FIHC is that there exist some common words for each cluster. FIHC use such words to cluster documents and build hierarchical topic tree. In this thesis, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

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