公开(公告)号：US11210348B2

公开(公告)日：2021-12-28

申请号：US16396682

申请日：2019-04-27

Applicant: HUIZHOU UNIVERSITY

Inventor： Jinqiu Huang ,  Deming Xu ,  Changlin Wan

IPC: G06F16/906 ,  G06F17/18 ,  G06K9/62

Abstract: The present disclosure provides a data clustering method based on K-nearest neighbor, which sorts data points to be clustered in ascending order according to the maximum radiuses of K-nearest neighbors of the data points, that is, according to the density, and perform the first pass across the data points after sorting the data points in ascending order to incorporate the data points that conform to the statistical similarity into the same cluster; then perform the second pass across the data points with smaller cluster density according to the scale required during the clustering to find out all noise points and incorporate non-noise points into the nearest large-density cluster, so as to realize data clustering, which has the benefits of no need to preset the number of clusters and know the probability distribution of the data and convenience to set parameters.

公开(公告)号：US20190251121A1

公开(公告)日：2019-08-15

申请号：US16396682

申请日：2019-04-27

Applicant: HUIZHOU UNIVERSITY

Inventor： Jinqiu Huang ,  Deming Xu ,  Changlin Wan

IPC: G06F16/906 ,  G06K9/62 ,  G06F17/18

CPC classification number: G06F16/906 ,  G06F17/18 ,  G06K9/6215 ,  G06K9/6221 ,  G06K9/6223

Abstract: The present disclosure provides a data clustering method based on K-nearest neighbor, which sorts data points to be clustered in ascending order according to the maximum radiuses of K-nearest neighbors of the data points, that is, according to the density, and perform the first pass across the data points after sorting the data points in ascending order to incorporate the data points that conform to the statistical similarity into the same cluster; then perform the second pass across the data points with smaller cluster density according to the scale required during the clustering to find out all noise points and incorporate non-noise points into the nearest large-density cluster, so as to realize data clustering, which has the benefits of no need to preset the number of clusters and know the probability distribution of the data and convenience to set parameters.