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Clustering by Finding Average Density

Published: 26 October 2020 Publication History

Abstract

Density Peak Clustering (DPC) algorithm can get better clustering results of data sets with lower dimensions. However, for the high-dimensional data sets, there are many nodes in the clustering center area; their densities are relatively high and close to each other, so that it is hard to identify the centering node accurately. It is found that the accuracy of the traditional DPC algorithm decreases terribly with the increasing of data set dimensions. In order to deal with the indistinguishable density problem, we propose a new clustering method, called Clustering by Finding Average Density (CFAD), which can enhance the clustering effect of high-dimensional data sets. Experiments show that the proposed algorithm outperforms DPC for both the artificial and the real data sets.

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AIAM2020: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture
October 2020
566 pages
ISBN:9781450375535
DOI:10.1145/3421766
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2020

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Author Tags

  1. Average
  2. Cluster
  3. Density peak

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AIAM2020

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AIAM2020 Paper Acceptance Rate 100 of 285 submissions, 35%;
Overall Acceptance Rate 100 of 285 submissions, 35%

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