Name
Title | ||
|---|---|---|
Hierarchical Topology-Based Cluster Representation for Scalable Evolutionary Multiobjective Clustering |
Abstract | ||
|---|---|---|
Evolutionary multiobjective clustering (MOC) algorithms have shown promising potential to outperform conventional single-objective clustering algorithms, especially when the number of clusters
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is not set before clustering. However, the computational burden becomes a tricky problem due to the extensive search space and fitness computational time of the evolving population, especially when the data size is large. This article proposes a new, hierarchical, topology-based cluster representation for scalable MOC, which can simplify the search procedure and decrease computational overhead. A coarse-to-fine-trained topological structure that fits the spatial distribution of the data is utilized to identify a set of seed points/nodes, then a tree-based graph is built to represent clusters. During optimization, a bipartite graph partitioning strategy incorporated with the graph nodes helps in performing a cluster ensemble operation to generate offspring solutions more effectively. For the determination of the final result, which is underexplored in the existing methods, the usage of a cluster ensemble strategy is also presented, whether
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is provided or not. Comparison experiments are conducted on a series of different data distributions, revealing the superiority of the proposed algorithm in terms of both clustering performance and computing efficiency. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCYB.2021.3081988 | IEEE Transactions on Cybernetics |
Keywords | DocType | Volume |
Clustering,ensemble strategy,multiobjective optimization,number of clusters,representation learning | Journal | 52 |
Issue | ISSN | Citations |
9 | 2168-2267 | 2 |
PageRank | References | Authors |
0.35 | 46 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuwei Zhu | 1 | 3 | 1.03 |
| Lihong Xu | 2 | 344 | 36.70 |
| Erik Goodman | 3 | 145 | 15.19 |