本文提出了一個新的分群架構。運用此架構，我們可以使執行時間相較於原本的方法有大幅度的縮短。由於文件資料集通常是數量龐大的高維度資料，導致了一般的分群方法需要不少時間運算。在我們的方法中，同時使用了分群方法與維度縮減的方法並行處理。首先，我們把資料分割成幾個部份，取出其中一份作分群後依分群結果做維度縮減。接下來再加進一部分資料，與原來的部份結合並轉換為縮減過後之維度，重複分群及維度縮減的動作，如此循環直至所有分割的資料均處理完畢。因為有對資料做分割及維度縮減，故我們可以處理原本的分群法不能處理的大量資料。而利用不同分群方法與維度縮減方法的結合，我們可以對分群所需時間做一定程度上的改善。

Peoples pay more and more attention on document clustering methods. Because of the high dimension and the large number of data, clustering methods usually need a lot of time to calculate. We propose a scheme to make the clustering algorithm much faster then original. We partition the whole dataset to several parts. First, use one of these parts for clustering. Then according to the label after clustering, we reduce the number of features by a certain ratio. Add another part of data, convert these data to lower dimension and cluster them again. Repeat this until all partitions are used. According to the experimental result, this scheme may run twice faster then the original clustering method.

摘要 i
Abstract ii
目錄 iii
圖目錄 iv
表目錄 v
第一章 簡介 - 1 -
第二章 文獻探討 - 5 -
2.1 向量空間模型 - 5 -
2.2 分群的方法 - 6 -
2.2.1 k-means - 9 -
2.2.2 Isodata - 11 -
2.3 維度縮減 - 13 -
第三章 研究方法 - 15 -
3.1 研究動機 - 15 -
3.2 方法架構 - 16 -
第四章 實驗結果與分析 - 21 -
4.1 實驗：k-means - 23 -
4.2 實驗：isodata - 29 -
4.3 大型資料集 - 34 -
第五章 結論與展望 - 35 -
5.1 結論 - 35 -
5.2 未來發展 - 36 -
第六章 參考文獻 - 37 -

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