Name
Abstract | ||
|---|---|---|
Since Density Peak Clustering (DPC) algorithm was proposed in 2014, it has drawn lots of interest in various domains. As a clustering method, DPC features superior generality, robustness, flexibility and simplicity. There are however two main roadblocks for its practical adoptions, both centered around the selection of cutoff distance, the single critical hyperparameter of DPC. This work proposes an improved algorithm named Streamlined Density Peak Clustering (SDPC). SDPC speeds up DPC executions on a sequence of cutoff distances by 2.2-8.8X while at the same time reducing memory usage by a magnitude. As an algorithm preserving the original semantic of DPC, SDPC offers an efficient and scalable drop-in replacement of DPC for data clustering.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3358053 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
algorithm optimization, density clustering, hyperparameter tuning | Data mining,Information retrieval,Computer science,Cluster analysis | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuai Yang | 1 | 0 | 1.35 |
| Xipeng Shen | 2 | 2025 | 118.55 |
| Min Chi | 3 | 187 | 28.26 |