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- W4230254857 abstract "For classical clustering algorithms, it is difficult to find clusters that have non-spherical shapes or varied size and density. In view of this, many methods have been proposed in recent years to overcome this problem, such as introducing more representative points per cluster, considering both interconnectivity and closeness, and adopting the density-based method. However, the density defined in DBSCAN is decided by minPts and Eps, and it is not the best solution to describe the data distribution of one cluster. In this paper, a deviation factor model is proposed to describe the data distribution and a novel clustering algorithm based on artificial immune system is presented. The experimental results show that the proposed algorithm is more effective than DBSCAN, k-means, etc." @default.
- W4230254857 created "2022-05-11" @default.
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- W4230254857 date "2020-01-01" @default.
- W4230254857 modified "2023-09-25" @default.
- W4230254857 title "A novel clustering algorithm based on the deviation factor model" @default.
- W4230254857 doi "https://doi.org/10.1504/ijcse.2020.105725" @default.
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