Affiliations: [a] College of Computer and Information Science, Chongqing Normal University, Chongqing, China | [b] Chongqing Center of Engineering Technology Research on Digital Agricultural and Service, Chongqing, China | [c] Chongqing Master Station of Agricultural Technology Promotion, Chongqing, China | [d] Beibei Agricultural and Rural Committee, Chongqing, China
Correspondence:
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Corresponding author: Shaohua Zeng, College of Computer and Information Science, Chongqing Normal University, Chongqing, China. E-mail: [email protected].
Abstract: The conventional fuzzy C-means (FCM) is sensitive to the initial cluster centers and outliers, which may cause the centers deviate from the real centers when the algorithm converges. To improve the performance of FCM, a method of initializing the cluster centers based on probabilistic suppression is proposed and an improved local outlier factor is integrated into the model of FCM. Firstly, the probability of an object as cluster center is defined by its local density, and all initial centers are obtained by the cluster center’s probability and probability suppression function incrementally. Next, an improved local outlier factor is reconstructed according to the local distribution of an object, and its reciprocal is regarded as the contribution degree of an object to cluster center. Then, the improved local outlier factor is integrated into FCM to alleviate the negative effect caused by outliers. Finally, experiments on synthetic and real-world datasets are provided to demonstrate the clustering performance and anti-noise ability of proposed method.
Keywords: FCM, local outlier factor, initial cluster center, outlier
