硬聚类（hard clustering），把数据点划分到确切的某一聚类中，如K-均值聚类。而模糊聚类（亦称软聚类，soft clustering）中，数据点则可能归属于不止一个聚类中，并且这些聚类与数据点通过一个成员水平（实际上类似于模糊集合中隶属度的概念）联系起来。成员水平显示了数据点与某一聚类之间的联系有多强。模糊聚类就是计算这些成员水平，按照成员水平来决定数据点属于哪一个或哪些聚类的过程。

模糊C-均值算法（FCM）是应用最为广泛的模糊聚类算法之一。详见模糊C-均值算法。

Fuzzy clustering (also referred to as soft clustering) is a form of clustering in which each data point can belong to more than one cluster or partition.

Clustering or cluster analysis involves assigning data points to clusters (also called buckets, bins, or classes), or homogeneous classes, such that items in the same class or cluster are as similar as possible, while items belonging to different classes are as dissimilar as possible. Clusters are identified via similarity measures. These similarity measures include distance, connectivity, and intensity. Different similarity measures may be chosen based on the data or the application.