Corpus ID: 235422560

Evolutionary Robust Clustering Over Time for Temporal Data

@article{Zhao2021EvolutionaryRC,
  title={Evolutionary Robust Clustering Over Time for Temporal Data},
  author={Qi Zhao and Bai Yan and Yuhui Shi},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.07252}
}
In many clustering scenes, data samples’ attribute values change over time. For such data, we are often interested in obtaining a partition for each time step and tracking the dynamic change of partitions. Normally, a smooth change is assumed for data to have a temporal smooth nature. Existing algorithms consider the temporal smoothness as an a priori preference and bias the search towards the preferred direction. This a priori manner leads to a risk of converging to an unexpected region… Expand

References

SHOWING 1-10 OF 52 REFERENCES
Efficient evolutionary spectral clustering
TLDR
A stopping criterion based on the convergence of the cluster assignments after the selection of each pivot is used, which is effective also when there is not a fast decay of the Laplacian spectrum, and has low memory requirements because only matrices of size Nm and mm are constructed. Expand
Evolutionary Self-Expressive Models for Subspace Clustering
TLDR
This work introduces evolutionary subspace clustering, a method whose objective is to cluster a collection of evolving data points that lie on a union of low-dimensional evolving subspaces, and proposes a non-convex optimization framework that exploits the self-expressiveness property of the evolving data while taking into account representation from the preceding time step. Expand
On evolutionary spectral clustering
TLDR
This article proposes two frameworks that incorporate temporal smoothness in evolutionary spectral clustering and demonstrates that their methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems. Expand
Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions
TLDR
This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings and proposes three effective and efficient techniques for obtaining high-quality combiners (consensus functions). Expand
Evolutionary affinity propagation
  • N. Arzeno, H. Vikalo
  • Mathematics, Computer Science
  • 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2017
TLDR
The algorithm promotes temporal smoothness of the clustering solutions at distinct temporal snapshots by linking variable nodes of the graph across time, and is capable of detecting cluster births and deaths. Expand
A new multi-objective evolutionary framework for community mining in dynamic social networks
TLDR
The ability of the proposed multi-objective evolutionary clustering framework to address the problem more accurately than the existing state-of-the-art formulations is demonstrated. Expand
Hierarchical evolving Dirichlet processes for modeling nonlinear evolutionary traces in temporal data
TLDR
An online learning framework based on Gibbs sampling to infer the evolutionary traces of clusters over time is designed and validated that EDP and EHDP can capture nonlinear evolutionary trace of clusters on both synthetic and real-world text collections and achieve better results than its peers. Expand
A Survey of Multiobjective Evolutionary Clustering
TLDR
A comprehensive and critical survey of the multitude of multiobjective evolutionary clustering techniques existing in the literature, classified according to the encoding strategies adopted, objective functions, evolutionary operators, strategy for maintaining nondominated solutions, and the method of selection of the final solution. Expand
Robust Optimization Over Time: Problem Difficulties and Benchmark Problems
TLDR
Two robustness definitions in ROOT are analyzed and then two types of benchmark problems are developed, motivated by the inappropriateness of existing DOP benchmarks for the study of ROOT, respectively. Expand
Sparsity Learning Formulations for Mining Time-Varying Data
TLDR
Two formulations for evolutionary co-clustering and feature selection based on the fused Lasso regularization are proposed that can uncover shared features in clustering from time-evolving data matrices and consistently outperform prior methods. Expand
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