Sparse Dual of the Density Peaks Algorithm for Cluster Analysis of High-dimensional Data

Published

Conference Paper

© 2018 IEEE. The density peaks (DP) algorithm for cluster analysis, introduced by Rodriguez and Laio in 2014, has proven empirically competitive or superior in multiple aspects to other contemporary clustering algorithms. Yet, it suffers from certain drawbacks and limitations when used for clustering high-dimensional data. We introduce SD-DP, the sparse dual version of DP. While following the DP principle and maintaining its appealing properties, we find and use a sparse descriptor of local density as a robust representation. By analyzing and exploiting the consequential properties, we are able to use sparse graph-matrix expressions and operations throughout the clustering process. As a result, SD-DP has provably linear-scaling computation complexity under practical conditions. We show with experimental results on several real-world high-dimensional datasets, that SD-DP outperforms DP in robustness, accuracy, self-governess, and efficiency.

Full Text

Duke Authors

Cited Authors

  • Floros, D; Liu, T; Pitsianis, N; Sun, X

Published Date

  • November 26, 2018

Published In

  • 2018 Ieee High Performance Extreme Computing Conference, Hpec 2018

International Standard Book Number 13 (ISBN-13)

  • 9781538659892

Digital Object Identifier (DOI)

  • 10.1109/HPEC.2018.8547519

Citation Source

  • Scopus