當前已存在一些半監督式分群演算法，通過考量從使用者處搜集來的輔助資訊進行資料分群。輔助資訊主要分為兩個類別：一類是考量全域分群情況而存在的全域資訊，指明一些資料屬於某個特定的資料群；另一類是局部的連結資訊，指明兩個資料的關係，必須屬於同一群或者必須屬於不同群。
我們通過實驗發現當前通用的半監督式分群演算法仍舊存在缺陷，本文稱之為抽樣偏差。由於搜集輔助資訊的過程中使用者無法完全主動地表達分群觀點，抽樣搜集來的輔助資訊可能只包含到極少有代表性的資料，因此這些輔助資訊可能會對當前的演算法造成誤導，從而使演算法得到的分群結果與使用者真正想要的分群結果之間存在相當的偏差。
為瞭解決這個缺陷，我們提出了一種新的分群演算法，稱之為觀念轉換分析，我們在考量輔助諮詢的同時也考量使用者的觀念字句，並把使用者的觀念字句建立為觀念向量形式的模型。本論文主要研討考量局部連結資訊的半監督式分群問題，每一組觀念字句與一個局部的必須連結的約束同時搜集，每一個觀念向量描述與之對應的局部連結約束背後的使用者觀念。
為了驗證本文提出的演算法的有效性，我們使之與當前通用的其他半監督式分群演算法比較，使用真實資料集進行了大量實驗。實驗結果證實我們的觀念轉換分析分群演算法能夠有效地克服其他演算法的抽樣偏差缺陷，得到的分群結果更符合使用者觀念中的真實分群情況。

Several semi-supervised clustering algorithms have been proposed to create clusters by exploring side information collected from users. The side information mainly has two categories: one is seed indication information based on global cluster situation; the other is pairwise link constraint which is relatively local side information. This paper focuses on the latter: local side information.
We show in this paper there is still limitation of the current semi-supervised clustering algorithms. The side information that sampling collected from users may cover fewer representative instances, named as sampling bias here, which would mislead current algorithms and give rise to non-ignorable difference between identified clusters and the true clusters perceived by users.
To address the limitation, we present a new clustering algorithm, named perception transform analysis (PTA), taking user’s perception words together with traditional side information into account by modeling user’s perception words in the form of perception vectors. This paper focuses on local side information, which means each perception vector models the concepts behind a must-link constraint and can be collected from users together with must-links.
To verify the effectiveness of the proposed algorithm, we compare it with the state-of-the-art semi-supervised clustering algorithms. Extensive experiments are conducted on real datasets and the results demonstrate its advantages and robustness to sampling bias.

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