Hui Du, Zhiyuan Hu*, Depeng Lu, Jingrui Liu
CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3239-3259, 2024, DOI:10.32604/cmc.2024.046314
Abstract Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets with uneven density. Additionally, they incur substantial computational costs when applied to high-dimensional data due to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset and compute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similarity matrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a vote for the point with the highest density among its KNN. By utilizing the vote counts More >
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