傳統向量模式檢索系統回傳的相關資料往往過於雜亂缺乏系統，使用者必須花費心思逐步過濾，才能取得真正符合需求的資訊。本研究以聚合法所建構出的樹狀結構為基礎，由下而上動態群聚向量模式檢索系統所回傳的結果，形成多個群集，群集間依本研究之耦合力與內聚力的平均值做排名，群集內則依文章與查詢的相似度做排名，經調整排名後提升其精確率，並以群集的方式提供使用者瀏覽。 本研究採用中文文件集，經斷詞、特徵詞選取、建立文件向量、分群、檢索、群聚檢索結果與調整排名等處理。實驗結果顯示，在整體檢索表現中本系統可提升傳統向量模式檢索系統約20.9%~24.0%的精確率，經Wilcoxon Signed Ranks Test檢定，在1個關鍵詞與2個關鍵詞查詢下，本系統檢索表現優於傳統向量模式檢索系統。 Usually the document ranking returned by the traditional vector space model of an information retrieval system is unorganized. It is often found that related documents do not have adjacent ranks. In order not to miss the needed information, the user still has to read several unrelated documents before finding another related document. In this research, we cluster the documents from the traditional vector space model based on the binary tree hierarchy constructed by the AGglomerative NESting (AGNES) algorithm. The clusters are ranked by the average of the coupling and the cohesion measures proposed in this thesis, and the documents in the cluster are ranked by the similarity between the query and the document. We try to improve the precision by such ranking adjustment. We used the Chinese news dataset and went through the word segmentation, vector representation, AGNES clustering, query based document retrieval and the final ranking adjustments for evaluation. As result, our system can improve the precision by 20.9% to 24.0% compared to the traditional vector space model. We also tested the result by the Wilcoxon Signed Ranks Test. It shows that our system is significantly better than the traditional vector space model for queries of one or two keywords.
