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Erschienen in:
2017 | OriginalPaper | Buchkapitel
A Potential-Based Density Estimation Method for Clustering Using Decision Graph
verfasst von : Huanqian Yan, Yonggang Lu, Li Li
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2017
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Abstract
Clustering is an important unsupervised machine learning method which has played an important role in various fields. As suggested by Alex Rodriguez et al. in a paper published in Science in 2014, the 2D decision graph of the estimated density value versus the minimum distance from the points with higher density values for all the data points can be used to identify the cluster centroids. However, the traditional kernel density estimation methods may be affected by the setting of the parameters and cannot work well for some complex datasets. In this work, a novel potential-based method is designed to estimate density values, which is not sensitive to the parameters and is more effective than the traditional kernel density estimation methods. Experiments on several synthetic and real-world datasets show the superiority of the proposed method in clustering the datasets with various distributions and dimensionalities.