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首页> 外文会议>2018 International Conference on Intelligent Systems and Computer Vision >Possibilistic fuzzy C-means clustering under observer-biased framework
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Possibilistic fuzzy C-means clustering under observer-biased framework

机译：观察者偏向框架下的可能性模糊C均值聚类

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摘要

Ensuring an adaptable and interactive tools to analyze data objects is an advisable objective of machine learning algorithms. Many methods exist, and new methods, or improvements in existing ones are proposed regularly to deal with a variety of problems in different areas. We develop a variant of the well-known Possibilistic Fuzzy c-Means Clustering algorithm PFCM that takes into account the observer-biased framework, Possibilistic fuzzy c-means with focal point PFCMFP. the accuracy of the proposed method is verified by cluster validity measures. The experimental results have shown that the accuracy of the new method increases significantly, compared to the initial PFCM algorithm. To elaborate this study, we have used a dataset of individual household electric power consumption, that is accessed publicly at the UCI Machine Learning Repository.
机译：确保自适应的交互式工具来分析数据对象是机器学习算法的明智目标。存在许多方法，并且定期提出新方法或对现有方法进行改进以应对不同领域中的各种问题。我们开发了一种著名的可能模糊c均值聚类算法PFCM的变体，该算法考虑了观察者偏见的框架，即带有焦点PFCMFP的可能模糊c均值。通过聚类有效性度量验证了所提方法的准确性。实验结果表明，与初始PFCM算法相比，该新方法的准确性显着提高。为了详细说明这项研究，我们使用了个人家庭用电量的数据集，该数据集可在UCI机器学习存储库中公开访问。

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