隨著資訊傳遞速度的增加，消費者的需求變動頻繁致使許多企業追求較短的產品生命週期；因此，產生了資料量不足的問題而不易對產品進行統計分析。半監督式學習利用少量已標記資料所提供的知識來分析大量的未標記資料。本研究提出的一項半監督式學習法利用已標記資料做為種子來產生初始群心進行未標記資料的分群稱為Seeded-PCM法。研究中使用可能性隸屬函數來代表一個資料點隸屬於某一群集的程度，並藉由可能性隸屬函數的優是找出資料中的離群值將以排除藉以提升結果的可性度。在分群之後，再使用分群驗證指標來決定一個群集是否需要再進行切割。本研究提供一項新的半監督式學習法，並且證明了此方法的分類正確率高於其他半監督式學習法及一般常用的分類方法。本方法不需要設定初始的分群群數，利用分群驗證指標將可以決定最適合的切割狀況。

As the speed of information transferring becomes faster than before, the demand of consumers changes frequently such that many companies are forced to pursue a short product life cycle to enhance competitiveness. However, data collected in the pilot run stage are usually small and raise the so-called problem of insufficient data analysis. Semi-supervised learning provides a way to analyze unlabeled data using the knowledge provided by few labeled data. To further improve this method, before partitioning the unlabeled data, this research provides a method, Seeded-Possibilistic CMeans, to generate initial cluster centroids using the labeled data as seeds, where possibilistic membership degree is employed to represent the possibility level of a data point that belongs to a cluster. By the advantage of possibilistic membership degree, we can find outliers and make the result more reliable by removing it. Furthermore, this research applies cluster validation to decide whether a cluster needs to be divided or not.
This new developed way of semi-supervised learning can improve the classification accuracy than other semi-supervised learning methods with a benefit of no need to pre-determine a number of clusters before clustering.

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