Name
Abstract | ||
|---|---|---|
Compositional data refer to a vector with parts that are positive and subject to a constant-sum constraint. Examples of compositional data in the real world include a vector with each entry representing the weight of a stock in an investment portfolio, or the relative concentration of air pollutants in the environment. In this study, we developed a Convex Clustering approach for grouping Compositional data. Convex clustering is desirable because it provides a global optimal solution given its convex relaxations of hierarchical clustering. However, when directly applied to compositions, the clustering result offers little interpretability because it ignores the unit-sum constraint of compositional data. In this study, we discuss the clustering of compositional variables in the Aitchison framework with an isometric log-ratio (ilr) transformation. The objective optimization function is formulated as a combination of a L-2-norm loss term and a L-1-norm regularization term and is then efficiently solved using the alternating direction method of multipliers. Based on the numerical simulation results, the accuracy of clustering ilr-transformed data is higher than the accuracy of directly clustering untransformed compositional data. To demonstrate its practical use in real applications, the proposed method is also tested on several real-world datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00500-020-05355-z | SOFT COMPUTING |
Keywords | DocType | Volume |
Compositional data analysis, Aitchison geometry, Convex clustering, Alternating direction method of multipliers (ADMM) | Journal | 25 |
Issue | ISSN | Citations |
4 | 1432-7643 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaokang Wang | 1 | 0 | 1.69 |
| Huiwen Wang | 2 | 29 | 14.15 |
| Zhichao Wang | 3 | 0 | 0.34 |
| Jidong Yuan | 4 | 18 | 6.45 |