在資料探勘領域中，群集分析(cluster analysis)為資料預處理的重要方法，而在群集分析中的最常使用的方法之一，為K-means演算法，K-means分群法是以歐幾里得距離(Euclidean distance)作為分群依據，雖然使用歐幾里得距離能夠體現個體數值特徵的絕對差異，但如果發生距離非常接近甚至相同時，可能產生難以判斷分群的結果。而由K-means演算法衍生而來的Fuzzy C-means演算法，透過模糊理論的概念，以隸屬程度來表現出每個物件屬於各群集的程度，但隸屬程度的判斷仍然只依靠歐幾里得距離，可能會產生與上述相同的問題。此外，餘弦相似度(cosine similarity)也是常被採用的度量方法之一，當使用餘弦相似度來衡量資料間相似度的大小時，由於餘弦相似度只單獨從方向性上區分差異，對於絕對數值並不敏感，仍有其盲點與缺陷存在。
因此，本研究希望對上述不足之處進行探討，希望改善K-means及Fuzzy C-means演算法，探討歐幾里得距離與餘弦相似度兩種衡量方法的特性，使分群演算法同時將兩者作為分群依據，發展出θ-means演算法與Fuzzy θ-means演算法來改善上述之問題。

Clustering also called unsupervised learning approach, is an important and fundamental task in data mining. This approach aims to divide data into groups of similar objects. K-means is a popular method of clustering methods that partitions n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster based on the Euclidean distance. Fuzzy C-means method was thus proposed to resolve this problem that uses the concept of fuzzy theory with the membership degree, thus allows data to belong to several groups. However, Fuzzy C-means method is only able to partially problem because it still relies on the Euclidean distance in the data-partitioning process. Cosine similarity method has been broadly used to measure the similarity degree among data patterns by distinguish the difference among data based on direction. Hence, this cosine similarity is not affected by the Euclidean distance. Combining K-means clustering and Fuzzy C-means clustering with cosine similarity may bring efficient solution to handle the Euclidean distance-related problems. The objective of this study is to discuss the shortcomings of the above-mentioned methods and to establish two newly unsupervised learning models, including θ-means algorithm and fuzzy θ-means which are hybrids of K-means and Fuzzy C-means clustering with cosine similarity method, respectively.

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