A Density Peak Clustering algorithm based on Adaptive K-nearest Neighbors with Evidential Strategy
Authors: 
Fengjun Ji, Lin Li, Tao Zhang, Bin Zhang, Junbo Yang, Jie Yin, Qian WangAuthors Info & Claims
Fengjun Ji, Lin Li, Tao Zhang, Bin Zhang, Junbo Yang, Jie Yin, Qian WangAuthors Info & ClaimsPages 166 - 171
Abstract
A novel clustering method based on density peaks(DPC) was published on the journal Science. However, for DPC, it is a challenge to choose an appropriate cutoff distance dc, which can affect local density distribution of the datasets. Additionally, the assigning strategy in DPC may cause a domino effect: Many following points might be wrongly assigned once a point is assigned erroneously. To overcome these drawbacks, we introduce a density peak clustering method based on adaptive K-nearest neighbors with evidential assigning strategy (named as AKE-DPC). Firstly, we introduce an adaptive K-nearest neighbors strategy to find a proper K which can form a reasonable local density distribution and find cluster centers simultaneously; secondly, after the cluster centers are found, we introduce a strategy based on evidential rules to assign the remaining points. The advantages of AKE-DPC are obvious that negate the need for decision graph and input parameter such as p, a percentage which is used to computer the cutoff distance dc in DPC. Experiments on both synthetic and real-world datasets are conducted to compare our algorithm with both DPC and some DPC-KNN algorithms such as ADPC-KNN, FKNN-DPC and DPC-KNN. The clustering results demonstrate that our algorithm is not only effective but also outperforms other algorithms on most cases.
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Published In
December 2022
341 pages
ISBN:9781450397773
DOI:10.1145/3577530
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Published: 30 March 2023
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CSAI 2022
CSAI 2022: 2022 6th International Conference on Computer Science and Artificial Intelligence
December 9 - 11, 2022
Beijing, China
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