Fuzzy Clustering Algorithm with Ranking Features and Identifying Noise Simultaneously
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摘要: 提出了一种加权模糊聚类算法, 其优势在于能在实现有效聚类的同时, 对样本噪音进行识别和按样本特征对聚类的贡献程度进行排序. 因此, 本文所提出的方法具有鲁棒性, 并可对所得的特征排序进行特征选择, 实验结果表明了该方法具有上述优势.Abstract: This paper proposes a weighted fuzzy clustering algorithm (FCA) that can identify the noise of samples and rank the samples' features simultaneously according to their contribution degrees, while realizing clustering efficiently. Therefore, the FCA is robust and can be used to extract the sample's features. Experimental results indicate the above advantages of the FCA.
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Key words:
- Fuzzy clustering /
- convergence /
- weights /
- robustness
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