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Supervised kernel density estimation K-means
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DOI：10.1016/j.eswa.2020.114350.png)
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Abstract
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K-means is a well-known unsupervised-learning algorithm. It assigns data points to.. 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.
Keywords:
K-means
Supervised learning
Kernel density estimation
Incremental semi-supervised learning
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