Sparse Dual of the Density Peaks Algorithm for Cluster Analysis of High-dimensional Data
| Autor: | Xiaobai Sun, Tiancheng Liu, Dimitris Floros, Nikos P. Pitsianis |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Clustering high-dimensional data Computational complexity theory Computer science Image segmentation 03 medical and health sciences 030104 developmental biology Robustness (computer science) Computation complexity MATLAB Cluster analysis computer Algorithm computer.programming_language |
| Zdroj: | HPEC |
| DOI: | 10.1109/hpec.2018.8547519 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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