本論文所探討之主題乃是針對一般所利用之集群方法，試著以整數規劃的方法來探討 集群分析的問題。 整數規劃之特性在於其所得之分組結果為真正的最佳解，而一般集群方法（如連鎖法 ，k 一平均數法）所得之結果僅是局部最佳解。 本文共分五章，第一章為緒論；第二章簡介一般集群方法；第三章建立四個整數規劃 的模型，俾用以解決不同需求之集群分析的問題；第四章實例探討，以某國中學生之 學科成績做為集群分析之變數，將每個學生依其成績高低而予以分組，並就一般集群 方法及整數規劃方法各作分析，並予比較；第五章為結論。全文共計一冊，約一萬五 仟字。

The World Wide Web has become a medium for people to share information. People use Web-based collaborative tools such as question answering (QA) portals, blogs/forums, email and instant messaging to acquire information and to form online-based communities. In an online QA portal, a user asks a question and other users can provide answers based on their knowledge, with the question usually being answered by many users. It can become overwhelming and/or time/resource consuming for a user to read all of the answers provided for a given question. Thus, there exists a need for a mechanism to rank the provided answers so users can focus on only reading good quality answers. The majority of online QA systems use user feedback to rank users’ answers and the user who asked the question can decide on the best answer. Other users who didn’t participate in answering the question can also vote to determine the best answer. However, ranking the best answer via this collaborative method is time consuming and requires an ongoing continuous involvement of users to provide the needed feedback. The objective of this research is to discover a way to recommend the best answer as part of a ranked list of answers for a posted question automatically, without the need for user feedback. The proposed approach combines both a non-content-based reputation method and a content-based method to solve the problem of recommending the best answer to the user who posted the question. The non-content method assigns a score to each user which reflects the users’ reputation level in using the QA portal system. Each user is assigned two types of non-content-based reputations cores: a local reputation score and a global reputation score. The local reputation score plays an important role in deciding the reputation level of a user for the category in which the question is asked. The global reputation score indicates the prestige of a user across all of the categories in the QA system. Due to the possibility of user cheating, such as awarding the best answer to a friend regardless of the answer quality, a content-based method for determining the quality of a given answer is proposed, alongside the non-content-based reputation method. Answers for a question from different users are compared with an ideal (or expert) answer using traditional Information Retrieval and Natural Language Processing techniques. Each answer provided for a question is assigned a content score according to how well it matched the ideal answer. To evaluate the performance of the proposed methods, each recommended best answer is compared with the best answer determined by one of the most popular link analysis methods, Hyperlink-Induced Topic Search (HITS). The proposed methods are able to yield high accuracy, as shown by correlation scores: Kendall correlation and Spearman correlation. The reputation method outperforms the HITS method in terms of recommending the best answer. The inclusion of the reputation score with the content score improves the overall performance, which is measured through the use of Top-n match scores.