K-means is a well-known unsupervised-learning algorithm. It assigns data points to $k$ clusters, the centers of which are termed centroids.

However, these centroids have a structure usually represented by a list of quantized vectors, so that kernel density estimation models can better represent complex data distributions. This paper proposes a k-means-based supervised-learning clustering method termed supervised kernel-density-estimation k-means. The proposed approach uses kernel density estimation for class examples inside each cluster to obtain a better representation of the data distribution. The algorithm constructs an initial model using supervised k-means with an equal seed distribution among the classes so that a balance between majority and minority classes is achieved. We also incorporate incremental semi-supervised learning into the proposed method. Experiments were conducted using publicly available benchmark datasets. The results demonstrated that, compared with state-of-the-art supervised methods, the proposed algorithm, which can also perform incremental semi-supervised learning, achieved highly satisfactory performance.

K-means是一种众所周知的无监督学习算法。它将数据点分配给$ķ$ 簇，其中心称为质心。

但是，这些质心具有通常由一系列量化向量表示的结构，因此内核密度估计模型可以更好地表示复杂的数据分布。本文提出了一种基于k均值的监督学习聚类方法，称为监督核密度估计k均值。所提出的方法将内核密度估计用于每个群集内的类示例，以获得数据分布的更好表示。该算法使用监督的k均值构造一个初始模型，其中监督的k均值在各类之间具有相等的种子分布，从而实现多数类和少数类之间的平衡。我们还将增量半监督学习纳入了提出的方法。使用公开的基准数据集进行了实验。结果表明，